[Purpose/Significance] Constructing a discourse system for library science with Chinese characteristics is pivotal to enhancing the discipline's international discourse power and cultural soft power. As a discipline originally introduced from the West at the turn of the twentieth century, Chinese library science has undergone a century-long exploration toward theoretical autonomy and academic independence. From Qichao Liang's 1925 call to "build China's own library science" to the contemporary formulation of the "Chinese library science discourse system," generations of Chinese library scholars have engaged in sustained intellectual efforts to adapt foreign theories to local contexts. It is essential to trace this historical trajectory because doing so reveals how an imported discipline gradually developed its own conceptual frameworks and modes of expression. Unlike previous studies that focused primarily on translating Western theories or examined isolated historical periods, this study adopts a holistic perspective on discourse system construction, spanning the entire century-long evolution from dependence to autonomy. The research holds significant theoretical value for advancing the innovative development of Chinese library science discourse in the new era. It enhances the cultural soft power of China's library profession. It contributes to the broader prosperity of philosophy and the social sciences with Chinese characteristics. [Method/Process] This study employs a combined methodology of historical documentary analysis and theoretical analysis. Using the academic evolution of the past century as a framework, this study systematically examined library science literature from the Republican period, the early People's Republic of China, and the reform and opening-up era. This examination is based on a comprehensive collection and critical analysis of primary sources, including academic journals, monographs, policy documents, and professional standards. The analytical framework organized the construction achievements into three hierarchical levels: core discourse, fundamental discourse, and specific discourse. Core discourse refers to statements addressing the essential nature and value orientation of the discipline, encompassing interpretations of libraries' social functions, service missions, and developmental directions. Fundamental discourse constitutes the shared language of the academic community, including disciplinary foundation discourse, research methodology discourse, and professional norm discourse. Specific discourse directly serves practical operations, covering resource development, reader services, and technology application. This three-level framework draws upon general paradigms from philosophy and social science discourse studies while accommodating the distinctive characteristics of library science as a discipline where theory and practice interact dynamically. Building upon this hierarchical analysis, the study further investigates the construction pathways and reveals the underlying logic, providing a comprehensive account of how Chinese library science discourse has evolved structurally and functionally over the past century. [Results/Conclusions] The study found that the construction of China's library science discourse system follows a triple progressive pathway of translation, reconstruction, and fusion innovation. During the translation phase, the Western library science theories were selectively introduced through translated works and returning scholars, providing crucial intellectual resources for transforming traditional book repositories into modern libraries and establishing conceptual foundations for public service. In the reconstruction phase, Chinese scholars reinterpreted existing discourses in light of local institutional environments, producing indigenous theoretical expressions such as the "element theory," developing localized classification systems, and establishing socialist library service principles. The fusion innovation phase represents the advanced stage where original discourse production is achieved through integrating diverse resources, yielding formulations such as "information resource development," "smart libraries," and "library-based targeted poverty alleviation" that embody Chinese characteristics while maintaining international translatability. The study further reveals five dimensions of the underlying logic: the core mission is developing library science with Chinese characteristics; the principal task is continuously improving characteristic theories; the endogenous driver is the deepening of people-centered practice; the solid foundation is inheriting excellent Chinese culture; and the basic modality has shifted from intellectual dominance to state discourse dominance. For the new era, this paper proposed the following: 1) development should uphold the dialectical unity of theoretical autonomy and open inclusiveness. 2) There should be stronger synergistic resonance between state and academic discourses. 3) Technological and humanistic discourses should be deeply integrated. 4) Traditional discourse should be creatively transformed into modern forms. 5) Tthe dialogue mechanism between indigenous and international discourses should be refined.

[Purpose/Significance] Generative artificial intelligence is reshaping the ways in which information is accessed, organized, generated, evaluated, and applied. In the field of information resources management, this transformation represents not only a technological change, but also a paradigm shift that necessitates a re-evaluation of its research objectives, theoretical principles, service models, and institutional obligations. Against the background of constructing an independent knowledge system for philosophy and social sciences with Chinese characteristics, this paper takes the transition from human-computer interaction to human-AI collaboration as its central perspective. It aims to clarify how information resources management in China can incorporate AI into the internal logic of disciplinary reconstruction, rather than treating it merely as an external tool for improving efficiency. The study highlights the theoretical and practical significance of building concepts, frameworks, and service systems rooted in Chinese practice while maintaining the capacity for global academic dialogue. [Method/Process] This study adopts methods of theoretical interpretation and literature review. With a focus on four research threads - paradigm transition, core dimensions, system reconstruction, and practical pathways - this analysis explores the role of human-AI collaboration in developing an independent knowledge system for managing information resources in China. Specifically, it examines the shift from command-driven interaction to understanding intentions, from tool-based assistance to cognitive partnership, and from interface response to intelligent coupling. It also discusses the value, resource, and governance foundations of disciplinary construction, the reconstruction of knowledge organization and semantic services, and the practical approaches of discourse creation, interdisciplinary integration, and talent cultivation. [Results/Conclusions] The study concludes that the construction of an independent knowledge system for information resources management in China should take human-AI collaboration as a key entry point and proceed through three interrelated dimensions: core dimensions, system reconstruction, and practical pathways. In terms of its core dimensions, the discipline should provide three key areas of support: resources, services, and ethics. Chinese cultural resources should be transformed from retrievable resources into collaborative corpora that provide a semantic foundation of Chinese discourse, Chinese narratives, and Chinese cultural meanings. Knowledge services should move from general information provision to collaborative knowledge services oriented toward national strategic needs, thereby developing trustworthy, controllable, verifiable, and traceable vertical service systems. Disciplinary ethics should shift from instrumental rationality to people-centered collaborative ethics, safeguarding human agency, professional judgment, critical reflection, and accountability in intelligent knowledge production and public decision-making. In terms of system reconstruction, the discipline should promote three structural transformations. Knowledge association should move from static organization to collaborative generation, so that knowledge resources can support semantic understanding, evidence integration, and responsible content generation. Interaction mechanisms should move from interface response to semantic negotiation, enabling users and intelligent agents to jointly clarify intentions, compare evidence, refine questions, and form judgments. Service scenarios should move from general supply to vertical collaboration, embedding human-AI collaboration into smart libraries, archival intelligence, digital humanities, public knowledge services, enterprise intelligence, and other domain-specific contexts. Through these transformations, information resources management can move beyond resource management and information provision toward a human-AI collaborative knowledge production system for complex knowledge tasks. In terms of practical pathways, future development should focus on refining original concepts of human-AI collaboration, building interdisciplinary research mechanisms for human-AI collaboration, and cultivating digital-intelligent governance talents. These pathways can connect theoretical innovation, methodological integration, scenario-based validation, and institutional construction. Through sustained efforts in discourse creation, research organization, and talent development, information resources management in China can establish an independent disciplinary knowledge system with Chinese standpoint, Chinese semantics, and international explanatory power.

[Purpose/Significance] The development of an independent knowledge system has become an important issue in the advancement of philosophy and the social sciences in China. As an essential component of knowledge production and academic governance, the academic evaluation system directly influences research orientation, resource allocation, disciplinary development, and the formation of academic discourse. In recent years, although China has made continuous progress in academic evaluation reform, the current evaluation system still exhibits structural problems such as excessive dependence on external standards, overreliance on quantitative indicators, and insufficient support for original and long-term research. These issues not only weaken the guiding function of academic evaluation, but also restrict the development of an independent knowledge system with Chinese characteristics. Against this background, this study systematically explores the construction path of China's independent knowledge system for academic evaluation. Different from existing studies that mainly focus on specific evaluation tools or policy reforms, this study attempts to establish an integrated analytical framework combining evaluation paradigms, evaluation mechanisms, and supporting systems, thereby providing a systematic perspective for understanding the transformation of academic evaluation in China. [Method/Process] This study first examines the practical foundation of the development of academic evaluation in China from the perspectives of theoretical exploration, practical reform, technological empowerment, and policy support. On this basis, the study analyzes the structural problems of the current academic evaluation system from three dimensions: evaluation criteria, evaluation methods, and evaluation orientation. Specifically, the study argues that standardized and externally oriented evaluation criteria have constrained the expression of local academic characteristics; quantitatively dominated evaluation methods have limited the identification of originality, long-term contributions, and social value; and result-oriented evaluation mechanisms have weakened the guiding role of academic evaluation in promoting independent innovation. To address these issues, this paper constructs a three-level framework for the independent knowledge system of academic evaluation. At the paradigm level, the study proposes a transition from single-dimensional and result-oriented evaluation toward classified, multidimensional, transparent, and developmental evaluation paradigms. At the mechanism level, the study emphasizes the importance of peer review, classified evaluation, representative work systems, and the integration of quantitative and qualitative approaches in building collaborative and diversified evaluation mechanisms. At the structural level, the paper further discusses the roles of disciplinary systems, academic systems, and discourse systems in providing long-term support for the sustainable development of academic evaluation. In addition, this study takes the reform of applied journal evaluation in China as a practical case to examine how classification-based evaluation, multidimensional indicators, and digital technologies can promote the transformation of academic evaluation from external dependence toward independent development. [Results/Conclusions] The study found that the construction of an independent knowledge system for academic evaluation is not merely a technical adjustment of evaluation methods, but a systematic transformation involving value orientation, institutional operation, and structural support. Evaluation paradigm reconstruction provides the fundamental value orientation for academic evaluation reform; mechanism optimization serves as the operational bridge connecting evaluation concepts and evaluation practices; and disciplinary systems, academic systems, and discourse systems constitute the long-term structural foundation for the sustainable operation of the evaluation system. The case of applied journal evaluation demonstrates that classified evaluation, representative work systems, multidimensional indicators, and digital technologies can effectively enhance the adaptability and explanatory capacity of academic evaluation, thereby contributing to the establishment of a more autonomous and diversified evaluation system. However, the current construction of China's independent knowledge system for academic evaluation still faces challenges in terms of international dialogue, methodological innovation, evaluation transparency, and long-term dynamic assessment. Future studies should further explore the integration of digital technologies with evaluation theory, strengthen the international interpretability of China's academic evaluation discourse, and promote the formation of a more open, diversified, and sustainable academic evaluation system.

[Purpose/Significance] Following the renaming of the Information Resource Management (IRM) discipline from "Library, Information and Archives Management," there is an urgent need to construct an independent knowledge system that breaks free from Western theoretical pathways and develops an indigenous academic discourse system. Although the concept of "field", developed by the French sociologist Pierre Bourdieu, provides an important analytical tool for understanding the structural space of knowledge production and information exchange, its roots in the logic of capital and the framework of power analysis of Western capitalist societies mean that it is inadequate for fully revealing the inherent logic of Chinese-style knowledge production and social cognition. Therefore, the indigenization of IRM's theoretical field through the intellectual resources of Chinese philosophy has become a critical proposition for the discipline's autonomous development. The core categories within traditional Chinese academic discourse provide a profound intellectual foundation for the indigenization of the IRM theory. [Method/Process] Grounded in traditional Chinese academic discourse and adopting the fundamental stance of "taking Chinese learning as the essence," this paper employed a systematic approach to reinterpret the theoretical field of IRM from an indigenous perspective across five dimensions: spatial configuration, hierarchical structure, core dimensions, professional shaping, and disciplinary reconstruction. First, the paper examined the intellectual resources within traditional Chinese academic discourse that resonate with the concept of "field," reinterpreted Bourdieu's field theory from an indigenous standpoint, and proposed a Chinese interpretive framework for the "theoretical field" of the IRM. Second, based on the "V-Shape Model" (comprising the MTUI, DIKW, TASC, and EPGK sub-models) and the "Dual-Chain Interconstruction" theory developed by Dr. Wenjie Jie, the paper revealed the four-level spatial configuration of the IRM's theoretical field and its inherent logic, elucidating its implications for disciplinary development. Third, from the perspective of the "Dual-Chain Interconstruction" framework, the paper analyzed the characteristics of the theoretical field across four stages－pre-information stage, information stage, post-information stage, and "generalization" stage－revealing the evolutionary pattern of knowledge from disorder to order, from order to structure, from structure to semantics, and from semantics to intelligence. Fourth, the paper constructed a multi-dimensional analytical framework for the theoretical field from four core dimensions: user cognition, resource form, practical activity, and social mission. On this basis, taking libraries and archives management as typical professional scenarios, the paper elucidated the reverse shaping mechanism of professional practice in theoretical scenarios. It further demonstrated the systematic transformation pathway from theoretical field to educational practice through the case study of the curriculum reform for the Master of Library and Information Studies (MLIS) program at Renmin University of China. [Results/Conclusions] The research demonstrates that reinterpreting the theoretical field of the IRM through the lens of Chinese philosophical discourse can effectively transcend the "structure-agency" dichotomy inherent in Western sociological theory, providing a philosophical foundation for the construction of an independent disciplinary knowledge system grounded in the principles of "the inseparability of Dao and its manifestations," "the unity of heaven and humanity," "the unity of knowledge and action," and "the world belongs to all." The theoretical field system of the IRM with Chinese characteristics constructed in this paper reveals the intrinsic relationships among the spatial configuration, hierarchical structure, and core dimensions of the theoretical field, elucidates the practical pathways of professional shaping and disciplinary reconstruction, and provides a theoretical reference based on Chinese discourse for theoretical innovation and paradigm shift in the IRM discipline in the digital intelligence era. This research supports the discipline's transition from theoretical self-awareness to cultural confidence and contributes to the establishment of a national independent knowledge system.

[Purpose/Significance] This paper traces the historical formation and intellectual lineage of Chinese bibliography, extracting its core iconic concepts and the knowledge system they constitute. By analyzing the opportunities and challenges confronting the discipline in the contemporary era, this study aims to provide theoretical support for constructing an autonomous knowledge system of Chinese bibliography that is both self-sustaining in its Chinese character and open to global academic exchange. This research is significant because it attempts to reposition traditional Chinese bibliographic scholarship within the modern academic landscape. It does this by bridging classical heritage with contemporary information science, and by responding to the urgent need for a distinctively Chinese theoretical voice in the global humanities. [Methods/Process] Through systematic synthesis and conceptual analysis, this study identified and examined a set of foundational iconic concepts in Chinese bibliography, including jiaochou (collation and textual criticism), qiushu (methods of seeking and recovering lost texts), jieti (explanatory titles and interpretive annotations), zhulu (cataloging and bibliographic recording), zhushi (commentary and exegesis), fenlei (classification systems and modalities), and leixu (categorical prefaces and taxonomic introductions). These concepts collectively form a distinct knowledge system that has evolved over centuries, reflecting the unique intellectual traditions of Chinese textual scholarship. Building upon this conceptual foundation, the paper investigates the developmental pathways of Chinese bibliography in the context of the digital intelligence era. [Results/Conclusions] To develop a world-class Chinese bibliography that embodies distinctive Chinese characteristics and achieves international excellence, a multi-dimensional strategy is required. First, efforts must be firmly grounded in the preservation and promotion of excellent traditional Chinese culture, drawing on the rich heritage of classical bibliographic scholarship while reinterpreting it for contemporary needs. Second, the discipline must adhere to Marxist scientific principles to ensure that theoretical development remains critically informed, historically aware, and socially relevant. Third, the social service function of Chinese bibliography should be strengthened, particularly in guiding nationwide reading initiatives, fostering public cultural literacy, and supporting lifelong learning in an increasingly digital society. Fourth, deepening international cultural exchanges and open cooperation is essential for mutual learning between Chinese and foreign bibliographic traditions, enabling Chinese bibliography to contribute actively to global conversations while maintaining its distinctive identity. Fifth, greater emphasis should be placed on practical applications in guiding bibliographic work within specialized fields, such as rare book cataloging, digital archives, library technical services, and the organization of born-digital resources. Sixth, technological transformations centered on digital resources must be actively advanced. Digital bibliography should seek breakthroughs from four aspects: standard development, technology application, integration of theory and practice, and service expansion. Efforts should focus on promoting the standardization of digital resource description, utilizing cutting-edge technologies to achieve intelligent organization and processing of resources, strengthening the construction of cataloging databases, and extending services to meet cultural needs, thereby realizing a smart transformation. Taken together, these integrated measures constitute a coherent roadmap for achieving the creative transformation and innovative development of Chinese bibliography in the new era. Ultimately, this study advocates for constructing an autonomous knowledge system that balances disciplinary independence with openness to global scholarship, thereby ensuring that Chinese bibliography continues to thrive as both a heritage discipline and a forward-looking field of inquiry.

[Purpose/Significance] The release of the Trusted Data Space Development Action Plan (2024-2028) marks the strategic shift of data governance in China, and the trusted data space is positioned as the key infrastructure for the safe circulation and value realization of data elements. For university libraries, this national strategic deployment is not only an important opportunity to expand the service boundary, but also an inevitable requirement for their transformation from traditional document-based institutions to data-driven knowledge service institutions. At present, the research on trusted data space mostly focuses on the field of public libraries or pan-library, and research on university libraries with unique resource endowments, service traditions and institutional constraints is still scarce. This study aims to address a research gap by systematically exploring the integration of university libraries into trusted data space. It seeks to provide theoretical guidance and practical solutions for their transformation and development in the data age. [Method/Process] This study is guided by the action plan and adopts a multi-stage research approach. First, the trusted data space is systematically analyzed in four dimensions, core features, technical architecture, business activities, and governance mechanisms. Secondly, based on the public information on the official website of the library, the policy documents of the institutional repository and the publicly published literature, five representative university libraries in China and two international cases were selected for comparative analysis, and a four-dimensional analysis framework covering data resource construction, technology platform deployment, data governance mechanism and knowledge service innovation was constructed to evaluate their existing data capabilities. Thirdly, combined with the identified shortcomings, the transformation logic framework was constructed from the four dimensions of function, service, governance and value. Finally, the design includes three stages of start-up, construction and expansion, and refines seven practical paths: strategic guidance, data center governance, technology base construction, talent cultivation, service innovation, ecological coordination and value balance. [Results/Conclusions] The study found that university libraries have natural advantages in integrating into the trusted data space, including large-scale data resource aggregation capabilities, independent technology platform upgrade capabilities, scenario-based expansion of knowledge service capabilities, and institutional credibility. However, systematic ability still has four shortcomings. First, ownership of the data is unclear as it involves multiple right holders, such as authors, sponsors, libraries and database providers. Second, the standard connection is insufficient, and traditional metadata standards such as MARC and DC are difficult to adapt to data space standards such as DCAT and Schema.org. Third, the security mechanism is weak, and there is a lack of data classification, dynamic access control and the whole process audit. Fourth, the service model is limited, and the service is still mainly within the campus, lacking cross-domain expansion. In view of the above problems, this study proposed that university libraries should undergo a fundamental transformation in four areas. 1) A shift in focus from collecting resources to becoming a data hub. 2) A shift in services, from provding information to co-creating knowledge. 3) A shift in governance, from self-management to ecological synergy. 4) In terms of value orientation, realizing the dynamic balance between openness and security. The seven practical paths and the three-stage roadmap can provide operational implementation guidance for university libraries to break through the traditional service boundary, activate the potential of data elements, and deeply participate in the national data infrastructure and innovative ecological construction. Future research can empirically test and optimize the proposed framework through action research or pilot projects, and further explore the quantitative evaluation mechanism and value return model of data contribution.

[Purpose/Significance] The construction of an autonomous knowledge system for computational humanities is both a historical inevitability as this emerging interdisciplinary field matures in the data-driven, computational era. It is also a substantive response to the national strategy of developing Chinese characteristics-based philosophy and social sciences. Unlike previous prior surveys which treat digital humanities as a relatively unified field, this paper emphasizes the epistemological shift from "digital" to "computational" and elucidates how Chinese scholarship has developed and refined the concept of computational humanities since it was first named in 2021. Its significance lies in providing a systematic, three-dimensional framework for assessing the field's institutional maturity and in demonstrating how computational humanities can serve as a vehicle for the communication of the Chinese story and the conveyance of Chinese wisdom within a global perspective. [Method/Process] Building on a conceptual clarification of the differences between "computation" and "humanities," "data" and "digital," and "digital humanities" and "computational humanities," the study examined the field along three interrelated dimensions: the academic system, the disciplinary system, and the discourse system. At the academic level, the analysis traced the consolidation of "human expression"–human cultural activity recorded through symbolic systems–as the research object. This three-tier inquiry comprises "what happened," "what was expressed," and "how to evaluate". A five-layer methodological hierarchy was cosntructed, spanning datafication, semantic annotation, text mining, spatiotemporal and network analysis, and large-language-model-driven intelligent computation. The research paradigm is five-stage and governed by the dual criteria of technical reproducibility and humanistic interpretive force. At the disciplinary level, the study surveyed sub-disciplines formed under the "computational X" logic, alongside talent cultivation, journals, conferences, laboratories, and academic public accounts. The argument is grounded in five empirical case studies: empty-space ratios in Chinese landscape painting (618-2011), the historical origins of the Tunpu dialect, sentencing leniency in misdemeanor cases, the construction of a historical-bibliography knowledge base, and topic-modeling comparisons of pre-Qin Confucian classics. [Results/Conclusions] The findings indicate that the field of computational humanities has reached a strategic juncture for transitioning from fragmented practice to systematic development. The academic system has formed an internally coherent conceptual framework; the disciplinary system has acquired a recognizable map of "Computational X" branches; and the discourse system has produced an expressive structure unifying vertical hierarchies with horizontal connections, propelled by the strategic momentum of "AI+," "Data Elements ×," and the New Liberal Arts initiative. The limitations of this study include the incomplete systematization of the original theory, unsettled institutional affiliations, and limited capacity for international discourse. Our future research endeavors will be anchored in local issues and firmly embedded within the Chinese cultural milieu. This approach is expected to propel the autonomous refinement of the autonomous knowledge system through continuous, iterative processes of conceptual distillation and theoretical innovation.

[Purpose/Significance] This paper explores the theoretical attributes and functional pathways of Chinese bibliographical studies as a fundamental methodology for constructing an autonomous knowledge system in Chinese philosophy and social sciences, and outlines possible trajectories of future development. In an era that demands indigenous theoretical innovation, it is crucial to re-examine the methodological essence of Chinese bibliography, particularly its core principle of "distinguishing scholarly traditions and tracing the evolution of knowledge" (bianzhang xueshu, kaojing yuanliu), in order to establish a knowledge system with Chinese subjectivity. [Method/Process] This study investigated the unique methodological characteristics of Chinese bibliography, as well as the original academic positioning and specific functions of bibliographic methods in constructing an autonomous knowledge system in Chinese philosophy and social sciences. The analysis focuses on two key dimensions: 1) the intrinsic attributes of Chinese bibliography, including its humanistic scholarly characteristics, its integration with the spirit of modern scientific rationality, its reliance on documentary and objective knowledge, and the unity of practical and research methods; and 2) the positioning and functions of bibliography in constructing an autonomous knowledge system, particularly its role in interdisciplinary knowledge production, scholarly guidance for researchers, and exploration of academic frontiers. [Results/Conclusions] The study draws several conclusions. First, the Chinese bibliography should return to its core scholarly function of "distinguishing scholarly traditions and tracing the evolution of knowledge," serving as both a method and a product of academic research. This requires us to move beyond the modern divide between classical and descriptive bibliography, drawing on bibliographic compilation practices and their resulting outputs, such as catalogs and indexes, to explore the historiographical functions of modern bibliography. Second, we should explore specific academic disciplines to improve the compilation of subject bibliographies (zhuanke mulu) and the development of subject bibliographic studies. The compilation of subject bibliographies helps define disciplinary boundaries, construct knowledge structures, and shape academic trajectories in fields ranging from traditional humanities (e.g., literature and history) to emerging interdisciplinary areas. Third, we should develop guided-reading bibliographies and knowledge indexes to strengthen bibliography's function of showing pathways and guiding scholarly inquiry. This helps researchers to navigate information overload, identify core literature, and provide clear research pathways - a function that is largely underutilized in contemporary library OPAC systems. Fourth, we should conduct comparative Sino-foreign research into the history of Chinese bibliography. While Western bibliography emphasizes the scientific description of textual form and transmission, Chinese bibliography prioritizes the humanistic interpretation of textual content and value. This distinction offers profound insights into the development of an autonomous Chinese knowledge system. By studying the exchange of bibliographic knowledge between China and the West since the modern era, we can better understand their historical interaction and close relationship. Reaffirming and revitalizing the methodological value of bibliography signifies a return to the disciplinary roots of information resource management and a commitment to them. It also propels the field to reassert its irreplaceable academic value in the process of constructing an autonomous knowledge system in Chinese philosophy and the social sciences. These efforts will facilitate a fundamental shift from discourse based on Western paradigms to an autonomous Chinese scholarly approach. This shift will provide valuable historical insights and solid theoretical support for the broader endeavor of achieving intellectual self-reliance.

[Purpose/Significance] The rapid evolution of artificial intelligence (AI) has led to a surge in patent applications, placing immense pressure on traditional patent examination and management systems. While automated classification has gained attention, existing methods often suffer from "semantic drift," hierarchical conflicts, and a lack of interpretability, primarily because they treat classification as a flat probabilistic task rather than a structured logical inference. This study aims to develop a hierarchical automatic patent classification framework that is not only efficient but also hierarchy-consistent and deeply aligned with the professional logic of patent examiners. By shifting the paradigm from black-box probabilistic guessing to knowledge-driven steady-state inference, this study provides a scalable and reliable pathway for intelligent patent classification in high-density technical domains. [Method/Process] The proposed framework was built upon a three-stage mechanism: technical content extraction, technical theme condensation, and hierarchical mapping, utilizing DeepSeek-V3 as the core semantic engine. First, the study constructed an IPC classification standard library and a patent classification knowledge base. A key innovation here is the "Hierarchical Fusion Strategy," which explicitly encodes the examination logic by embedding parent-level technical definitions into child-level descriptions to provide a complete semantic boundary. This ensures that the model perceives the nested structure of the IPC system rather than treating categories as independent labels. Second, the framework performs a semantic-anchored extraction of technical information. Unlike traditional methods that rely on raw text, this process utilizes the IPC standard library as a reference to filter and condense patent claims and descriptions into structured "technical themes". This intermediate representation mitigates the risks of semantic hallucination and handles data sparsity by compressing the semantic space into a more consistent and discriminative form. Third, a "bottom-up" hierarchical mapping strategy was implemented. The system prioritizes matching at the most granular level (the IPC subgroup) and then derives higher-level categories through the established hierarchical chain. To ensure robustness, a dual-path verification mechanism - parallel comparison between independent matching and hierarchical mapping - was introduced. When results conflicted, the system employed a logic of confidence priority to perform local error correction, ensuring that the final output was both fine-grainedly accurate and hierarchically consistent. [Results/Conclusions] Experimental validation conducted on a dataset of Chinese AI invention patents from 2021 to 2025 demonstrates the superior "architecture stability" of the framework. The optimal fusion strategy achieved accuracy rates of 100%, 97.32%, 91.84%, 86.48%, and 71.25% at the IPC section, class, subclass, main group, and subgroup levels, respectively, significantly outperforming the PatentBERT baseline and direct large language model (LLM) classification. Ablation studies confirmed that the integration of IPC knowledge guidance, the condensation of technical themes, and the bottom-up mapping strategy are all critical contributors to performance gains. The results demonstrate that by encoding examination logic into the model, the inherent randomness of LLMs can be effectively constrained within a structured logical track. This framework essentially functions as a "classification skill" for AI Agents, capable of being integrated into intelligent examination systems via an API for constant and automated category updates. Despite limitations in domain coverage, the model-agnostic nature of the architecture suggests high potential for migration to other complex technical fields, providing a foundational methodology for the unified representation and analysis of multi-source innovation data.

[Purpose/Significance] To address the issues of knowledge dispersion, redundancy, and fragmentation within the organization of complex historical events, this study employs the event evolution graph method. It explores their potential applications in the semantic organization of historical events and the structured representation of historical knowledge. The aim is to enhance the discoverability, reusability, and semantic relevance of historical data, while broadening the theoretical framework and practical approaches of the event evolution graph in historical research. [Method/Process] First, we constructed a three-layer framework for complex historical event, consisting of a data layer, a semantic layer, and an application layer. The data layer enables the collection of all event elements and supports multimodal integration, including text, images, and maps. The semantic layer implements AI-enhanced event representation and deep relationship mining. The application layer performs correlation calculation and multi-level graph visualization, allowing users to interactively explore event structures and semantic pathways. On this basis, three key tasks were carried out. First, AI-enhanced methods for representing and extracting complex historical events were designed. In particular, a dynamic ontology model for AI understanding was constructed based on the W7 model, formally represented as:e = {what, why, who, how, which, when, where, environment, effect, certainty}. This formalization systematically depicts event elements and their semantic relationships, capturing not only basic event components but also contextual factors, causal consequences, and the degree of historical certainty. Second, AI-enhanced methods for calculating the correlation of complex historical events were proposed. These methods combine rule-based reasoning with machine learning classifiers to identify and quantify semantic relationships such as causality, temporality, correlation, and hierarchy among events. A case study on a representative complex historical event was conducted to validate the proposed framework and methods. [Results/Conclusions] The study demonstrates that AI can improve the accuracy of extracting event elements and analyzing semantic relationships. This provides a feasible technical pathway for organizing historical knowledge computationally and providing intelligent services. The case study results show that the generated event evolution graph captures multi-level event structures and reveals previously implicit causal and evolutionary patterns. However, research in this field still faces challenges, including the scarcity of high-quality historical corpora, subjectivity when generalizing and decomposing events, and insufficient integration of multimodal information. In the future, we will focus on three directions: developing weakly supervised learning and transfer learning methods tailored to scenarios with sparse historical data; designing human-computer collaborative tools for event decomposition and relationship annotation to balance automation with scholarly interpretability; and constructing multimodal event evolution graph for complex historical events by incorporating visual, spatial, and audio data.

[Purpose/Significance] The rapid advancement of Generative Artificial Intelligence (GAI) is fundamentally reshaping the landscape of knowledge production and dissemination, propelling information literacy education into a critical phase of paradigm reconstruction. However, contemporary university-level information literacy programs are often constrained by structural impediments, including pedagogical homogenization that fails to address individual learner differences, fragmented multimodal resources that hinder holistic cognitive development, and the "suspension" of ethical education, where abstract moral principles are difficult to internalize into concrete practice. These challenges severely restrict the transition of information literacy education from static skill transmission to dynamic, value-oriented cultivation. Therefore, exploring a novel educational model deeply empowered by GAI is theoretically significant for reconstructing the theoretical framework of "Cognition-Competence-Value" synergy framework and is imperative for cultivating responsible digital citizens who can think critically and make ethical decisions in the intelligent era. [Method/Process] To address these challenges, this study synthesizes three core theoretical pillars: Multimodal Cognitive Construction, Human-Computer Collaborative Evolution, and Value-Sensitive Design. This model, called the Three-Dimensional Spiral Model (3DSM), is centered on "Cognition-Competence-Value." This model posits a dynamic, mutually reinforcing mechanism in which these three dimensions spiral upward through continuous interaction. To empirically validate the model's efficacy, a rigorous 8-week quasi-experiment was conducted at China University of Petroleum (East China). The study involved 120 participants who were randomly assigned to experimental and control groups. The experimental group participated in an intervention based on the 3DSM that utilized advanced GAI technologies, including an improved CLIP model for multimodal alignment, a dynamic knowledge graph for personalized path planning, and a "value sandbox" for ethical simulations. The teaching design followed a spiral curriculum, progressing from "Multimodal Information Deconstruction" to "Human-Computer Collaborative Innovation," and finally to "Ethical Internalization." In contrast, the control group followed a traditional "lecture plus practice" model. A mixed-methods approach was employed for the evaluation. This approach combined quantitative metrics, such as retrieval efficiency logs and Jaccard similarity coefficients for accuracy, with the CTIC standardized test, which measures information awareness, tool application, and ethical cognition. The evaluation also included a qualitative analysis of learning artifacts and behavioral trajectories. [Results/Conclusions] The empirical findings demonstrate that the 3DSM significantly enhances learners' comprehensive information literacy. Statistically, the experimental group exhibited a 53% improvement in information retrieval efficiency compared to the control group, with a retrieval accuracy (Jaccard similarity) increase of 0.68 to 0.89. Furthermore, the accuracy rate of technical ethical decision-making reached 89.2%, and the effect size was substantial (Cohen's d=1.37), indicating a large practical impact. Mechanism analysis revealed three key drivers of this success. First, the improved cross-modal alignment optimized cognitive efficiency by enabling accurate deconstruction of heterogeneous resources. Second, the dynamic knowledge graph facilitated capability evolution through personalized, adaptive learning paths. Third, the "Ethical Pre-regulation" mechanism, where ethical constraints are applied at the onset of cognitive tasks, effectively resolved the "ethical suspension" problem by calibrating cognitive paths and preventing algorithmic bias. This research contributes to the field by providing a systematic, theoretical framework for the synergistic development of cognition, competence, and value in the GAI era. It offers libraries and educational institutions a replicable, evidence-based implementation pathway for deeply integrating GAI into their curricula, thereby transforming information literacy education into a dynamic ecosystem of human-machine symbiosis and value co-creation.

[Purpose/Significance] In response to the urgent need for deep digital transformation and the release of data value in smart libraries, this study is based on the research perspective of integrated application scenarios and data collaborative governance. It targets commonly encountered in smart libraries, such as fragmented data governance, insufficient collaboration among entities, weak scene adaptation, and the disconnection between theory and practice. This study breaks through the limitation of existing research that only regards scenarios as external application conditions. Taking scenario demands as the core driving force and internal logic for constructing governance mechanisms, it systematically explores the internal mechanisms and implementation paths of application scenarios that drive data collaborative governance. It further improves the theoretical system of smart library data governance and fills the academic gap in the research on integrating scenario-driven and collaborative governance. It also provides innovative ideas and theoretical support for enhancing the efficiency of allocating data resources, strengthening the enabling effect of smart services, and supporting the high-quality development of public cultural services. [Method/Process] With scenario theory and collaborative governance theory as the core theoretical basis, and by comprehensively applying methods such as theoretical deduction, framework construction, case empirical research, and normative research, this study analyzed the connotation and operational characteristics of application scenarios that drive data collaborative governance. It scientifically classified smart library applications into three types: core basic, value-added innovative, and emergency response. It also constructed a governance mechanism with five interlinked and differentiated subjects, objects, platforms, technologies, and systems adapted to different scenarios. It selected the Jiaxing City Library as an example to empirically verify, extracting and forming operational, replicable, and promotional governance strategies and implementation paths. [Results/Conclusions] The research indicates that there is a significant dynamic coupling, bidirectional iteration, and closed-loop evolution relationship between application scenarios, data collaborative governance mechanisms, and the development vision of smart libraries. The targeted allocation of governance elements is driven by scenario demands, and the iterative upgrade of scenarios is driven by governance effectiveness feedback. Efficient implementation is achieved through scenario design optimization, element resource allocation, data integration applications, and effectiveness evaluation feedback. The research verifies the scientific basis and practical feasibility of the theoretical framework. It can alaso provide valuable insights for enhancing the efficiency of smart library data governance and maximizing data value. This study is limited because as it only uses single-case empirical research. Further research can be carried out in the future in areas such as multi-case comparisons, cross-regional library collaborations, the deep integration of digital and intelligent technologies, and long-term governance mechanisms.

[Purpose/Significance] The rapid advancement of digital and artificial intelligence (AI) technology is profoundly transforming the functional positioning and service models of university libraries, shifting them from traditional resource management institutions to smart academic service hubs. This study aims to analyze the opportunities and challenges posed by digital and AI technology to university libraries, exploring how they can uphold their educational mission in the transition toward digitalization, ecological sustainability, and academic excellence, achieving the unity of technological empowerment and value preservation to promote sustainable library development. [Method/Process] The research followed the logical framework of "technological empowerment-risk assessment-value orientation," and conducted progressive and dialectical analysis. In the dimension of technological empowerment, it focused on three core transformation directions - resources, space, and platforms - to explore the ecological restructuring pathways of university libraries driven by digital and AI technologies. In the dimension of risk impact, it analyzed potential risks and value deviations arising from the application of digital and AI technologies across four aspects: reading cognition, service essence, resource development, and ethical privacy. In the dimension of value adherence, based on the core mission of university libraries, it proposed five value adherence dimensions: "people-centered, content-based, education-oriented, fairness-guided, and staff-focused". [Results/Conclusions] Digital technology has given university libraries new impetus in terms of resource integration, spatial reconstruction, and service upgrades. However, it has also brought risks and challenges, such as shallow reading, the instrumentalization of services, the homogenization of resources, and ethical and privacy issues. In the process of digitization, there is a close internal logic between technological empowerment, risk challenges, and value preservation in university libraries. Technological empowerment is the driving force for transformation, providing tools and paths for transformation, but its application requires value preservation as a prerequisite. Risk challenges are inevitable accompanying problems in the process of technological application, and they are a concrete manifestation of the contradiction between technological empowerment and value adherence. They need to rely on value adherence to guide technological direction and achieve dynamic balance. Value adherence is the key to maintaining the essence of a library, providing guidance for both technological empowerment and risk challenges, and ensuring that the library adheres to its original intention of "serving education and knowledge dissemination". University libraries need to find a balance among the three, using technology as a means, value as a guide, talent as support, and fairness as the bottom line, to construct an academic service model of "technology ecology value" collaborative development. This will enhance service efficiency and innovation potential while demonstrating the value of libraries in the high-quality development of higher education and the cultivation of talented professionals.

[Purpose/Significance] The rapid advancement of digital technologies has created substantial opportunities for promoting green agricultural transformation in China. Digital literacy plays a pivotal role in enabling large-scale farmers to understand and apply modern agricultural technologies, thereby shaping their willingness to adopt environmentally-friendly production practices. As the core actors in agricultural production, farmers' digital competence and their perception of new quality productive forces (PNQPF) directly influence how they respond to digital innovations and participate in green production. However, existing research has not yet established a systematic measurement framework for PNQPF, nor has it clarified the multi-stage cognitive mechanisms through which digital literacy affects farmers' green production willingness. [Method/Process] Drawing upon grounded theory and empirical investigation, this study adopted a mixed-method approach. First, in-depth interviews were conducted with diverse groups of large-scale farmers in northern and southern regions of the Xinjiang Uygur autonomous region, generating a rich textual corpus for qualitative analysis. Through open coding, selective coding, and theoretical coding, two fundamental cognitive dimensions - perception of labor tools and perception of labor objects - were identified, forming the basis of an eight-item PNQPF scale. In the quantitative stage, a structured survey was administered to 352 large-scale farmers across four provinces (Xinjiang, Shaanxi, Gansu, and Qinghai) andNingxia Hui Autonomous Region in Northwest China. The dataset encompasses demographic characteristics, operational features, digital literacy indicators, PNQPF perceptions, and evaluations of green production willingness. Exploratory and confirmatory factor analyses were employed to validate the construct reliability and structural robustness of the PNQPF scale, while regression-based mediation and moderation modeling enabled a systematic examination of the pathways, through which digital literacy influences green production willingness. This integrated analytical framework provides a comprehensive and evidence-based foundation for understanding farmers' decision-making processes in digital agricultural environments. [Results/Conclusions] The findings indicate that digital literacy significantly enhances farmers' willingness to adopt green production practices. Both cognitive dimensions of PNQPF - perception of labor tools and perception of labor objects - serve as key psychological mechanisms, exerting independent mediating effects and jointly forming a chain mediation pathway. This suggests that digital tools not only improve farmers' operational efficiency but also deepen their understanding of production objects, thereby reinforcing environmentally responsible behavior. Digital infrastructure further strengthens the impact of digital literacy on PNQPF, highlighting the importance of a supportive digital environment in amplifying farmers' behavioral transformation. Based on these insights, this study suggests that enhancing farmers' digital competence, improving regional digital infrastructure, and promoting targeted digital extension services are essential for advancing green agricultural development. Nevertheless, the sample is concentrated in Northwest China, where regional disparities in digital development and agricultural structure may limit the generalizability of the findings. Future research should expand the sample scope, incorporate longitudinal data, and explore the evolving role of digital technologies in shaping farmers' production decisions.

[Purpose/Significance] With the rapid development of technologies such as generative AI and knowledge graphs, digital libraries are undergoing transformative development. This study aims to address the evolving demand for knowledge services in digital libraries in the context of the development of AIGC technologies. The study seeks to resolve issues in traditional services, such as resource overload, mismatches between precise needs and available resources, and regional service disparities, to optimize public cultural knowledge service models for digital libraries. [Method/Process] This study employed a comprehensive approach combining online surveys, case studies, and literature review. First, through a literature review, the study systematically examined relevant theoretical findings, research hotspots, and cutting-edge developments in digital libraries, public cultural services, knowledge services, and AIGC technology applications both domestically and internationally. This process clarified the connotations and denotations of core concepts and established the theoretical foundation and analytical framework for the research. Second, using online research methods, the study comprehensively examined policy documents issued domestically and internationally regarding digital library development and the enhancement of public cultural services, systematically identifying the needs of digital libraries for public cultural knowledge services in the AIGC era. Finally, employing case analysis, the study systematically analyzed the core functions of AIGC-enabled digital libraries in delivering public cultural knowledge services, drawing on typical case studies from provincial and municipal-level libraries. [Results/Conclusions] This study clarified the intrinsic connection between AIGC technology and public cultural knowledge services provided by digital libraries. The findings indicate that in the AIGC era, digital libraries face three core requirements when delivering knowledge services for public culture. First is the need for AIGC-enabled resource allocation. This requires the use of AIGC technology to quickly classify, thoroughly process, and intelligently integrate large quantities of public cultural resources, thereby addressing issues of resource fragmentation and low utilization rates. Second is the need for secure and compliant technical tools. This requires the introduction of AIGC-related tools to optimize knowledge service processes and enhance service efficiency. Third is the need for human resources to ensure inclusive services. This requires cultivating multidisciplinary professionals with both library expertise and AIGC technical skills to support the continuous optimization of service models. Based on this, the study identified three core service functions for digital libraries in the AIGC era: the intelligent regional characteristics center, the intelligent cultural resources center, and the intelligent scientific research center, all geared toward public cultural knowledge services. The service model proposed by this study offers a reference framework for the transformation and upgrading of digital libraries at all levels, while also providing theoretical support and practical insights for the intelligent development of public cultural service systems.

[Purpose/Significance] This study examines how competitive intelligence (CI) is applied in enterprises driven by intellectual property (IP), using Pop Mart's Labubu as a case study. The primary purpose of this research is to understand how CI contributes to the formulation of brand strategies, market trend identification, user analysis, and the protection of intellectual property. By investigating how Pop Mart integrates CI into its operations, the study highlights the strategic importance of CI for decision-making processes and addresses a significant gap in the literature on the role of CI in the protection and management of IP in the context of emerging businesses. The significance of this research lies in its contribution to the broader understanding of competitive intelligence and its potential to guide IP-driven enterprises in enhancing their market positions and mitigating the risks associated with counterfeiting. [Method/Process] This research adopted a case study approach, focusing on Pop Mart's Labubu brand as the primary example of IP-driven business strategy. The study utilized the competitive intelligence cycle model as its theoretical framework, which emphasizes the continuous process of intelligence gathering, analysis, dissemination, and feedback. By analyzing how Pop Mart constructed and utilized its CI system, the study integrated both qualitative and quantitative research methods to examine the effectiveness of its competitive intelligence strategy. The qualitative analysis included in-depth interviews with key stakeholders at Pop Mart, as well as content analysis of relevant company reports and publicly available data on IP trends. The quantitative analysis involved the examination of market performance metrics, such as sales growth, brand awareness, and consumer sentiment, before and after the implementation of the CI strategy. The combination of these methods provided a holistic view of how CI supports Pop Mart's brand development and IP protection efforts. [Results/Conclusions] The findings of this study demonstrate that Pop Mart's use of competitive intelligence has significantly contributed to its brand success and IP protection. By systematically integrating CI into brand strategy, market analysis, and intellectual property management, Pop Mart has gained a competitive edge in the crowded market for cultural products. The company's strategic use of CI has allowed it to effectively identify market trends, understand consumer preferences, and monitor competitor activities, thus enabling more informed decision-making. Additionally, the robust IP protection system enabled by CI has reduced the risks associated with counterfeiting and trademark infringement, ensuring the sustainability of Labubu's market position. The study also highlights the importance of a multidimensional approach to IP protection, combining legal actions, technological monitoring, and fan-driven initiatives to safeguard intellectual property. Based on the findings, the research suggests that other IP-driven enterprises should prioritize the integration of competitive intelligence into their operational strategies, particularly in the areas of brand management and IP protection. The study acknowledges some limitations, such as the focus on a single case study, which may limit the generalizability of the findings to other industries or countries. Future research will expand the scope by comparing multiple case studies from different industries or geographic regions, examining the effectiveness of CI strategies in various contexts. Additionally, exploring the role of emerging technologies, such as artificial intelligence and blockchain, in enhancing CI efforts can provide valuable insights into the future direction of CI in IP management.

[Purpose/Significance] The development of artificial intelligence (AI) technology has brought both opportunities and challenges, redefining users' knowledge and capabilities. It is necessary to re-examine and build new capabilities suitable for the era of AI-AI literacy. As the main institution of information literacy education in higher education, university libraries have inherent advantages in carrying out AI literacy education. The research aims to explore the development path of AI literacy education in university libraries and assist them in better cultivating high-quality talent that meetS the needs of the AI era. [Method/Process] The practice of AI literacy education in university libraries is influenced by multiple interwoven factors and is essentially a complex, open and dynamic system. Therefore, introducing collaborative theory into this practical field provides guidance for the collaborative development of AI literacy education in university libraries in China. Through online research on the implementation of AI literacy education in libraries of 42 "Double First-Class" universities in China, a collaborative framework for AI literacy education in university libraries in China from the perspective of collaborative theory was constructed, starting from the educational subjects, educational objects, educational forms and educational contents, and the current collaborative status of each element was analyzed. [Results/Conclusions] The results show that the AI literacy education in domestic university libraries is characterized by the establishment of cooperative relationships by the main body, the emphasis on group mutual learning for the objects, the importance placed on platform complementarity in the form, and the focus on knowledge and skills in the content. Based on the collaborative framework and the research results, the following development paths are proposed: The first is the main body network layer, which requires the establishment of a diversified multi-party collaborative network, the creation of a specialized and composite team, and the improvement of a long-term collaborative management mechanism. The second is the object mutual assistance layer, which should be based on the two-way empowerment role positioning and the establishment of a two-way interactive education platform. The third is the form optimization layer, which should integrate online and offline educational carriers and design a classified and stratified curriculum system. The fourth is the content empowerment layer, which should develop a local framework for AI literacy theory, balance popularizing knowledge and deepening skills, and strengthen thinking cultivation and ethics education. Thus, AI literacy education will evolve from a scattered approach to a systematic one. It will shift from a single-subject focus to an integrated one, and from superficial to profound.

[Purpose/Significance] The formulation of the "15th Five-Year Plan" represents a pivotal strategic task for academic libraries in China. This plan coincides with a period of profound transformation, which is driven by the national agenda for high-quality development in higher education, as well as by the disruptive advancements in artificial intelligence (AI) and open science. This study aims to provide a reference for strategic planning and service innovation by systematically deconstructing the strategic plans of world-class university libraries. [Method/Process] This study employed a qualitative research design centered on document analysis. The research sample comprised the publicly available strategic planning documents of ten university libraries affiliated with institutions ranked within the top 50 of the QS World University Rankings 2026. A combination of text analysis and directed qualitative content analysis was applied to systematically deconstruct the sample. The analytical process involved three primary phases. 1) A formal text analysis was conducted to examine the planning documents' attributes, including their formulation bodies, planning cycles, and temporal scopes. 2) A coding framework consisting of ten strategic categories was deductively developed based on core elements of library strategic planning. 3) Using this framework, a detailed content analysis was performed. Every sentence or segment within the strategic texts that expressed a discrete strategic goal, initiative, or key action was identified as a unit of analysis, systematically coded, and categorized. This process enabled a two-dimensional examination: a frequency analysis of strategic foci and a more granular, qualitative interpretation of strategic objectives and specific measures. This methodological approach allowed for the extraction of both quantitative patterns and rich qualitative insights into the strategic direction and operational tactics of the world's leading academic libraries. [Results/Conclusions] The analysis reveals several key patterns and shifts in the strategic planning of world-class university libraries. Regarding formulation and governance, a parallel model is prevalent, featuring leadership from dedicated strategic offices or committees coupled with collaborative input from a multi-stakeholder body including staff, faculty, and students. Planning cycles are predominantly five years but are increasingly characterized by dynamic iteration, with provisions for mid-cycle reviews and updates, balancing long-term vision with operational agility. The substantive content of the strategies indicates a decisive transition from a focus on collection stewardship to an integrated knowledge service paradigm. Three dominant strategic thrusts are identified: 1) intelligent service ecosystem, emphasizing tiered information literacy, AI-embedded teaching support, and human-centered space design; 2) comprehensive research support, aiming to provide seamless services across the entire research life cycle－from idea generation and data management to publication, impact tracking, and public engagement; 3) extended social value creation, manifesting in commitments to digital inclusion, cultural heritage stewardship, community partnership, and environmental sustainability. Based on these findings, the study proposed a tailored pathway for Chinese academic libraries to formulate their "15th Five-Year Plan." Our recommendations include: 1) strengthening strategic governance by establishing a permanent planning unit to enable rolling revisions; 2) constructing an AI-enabled, integrated ecosystem that synergizes resources, services, and smart spaces; 3) establishing a data-driven, full life cycle research support chain; 4) building an open resource system and actively integrating into the global open science network; 5) expanding the social service matrix to concurrently address cultural heritage, inclusive access, and green transformation; and 6) deepening organizational capacity reform to foster a skilled, agile, and resilient workforce. A limitation of this study is its reliance on published planning texts, which may not fully capture the nuances of implementation challenges or internal organizational dynamics. Future research should employ mixed methods, such as surveys or interviews with library strategists, to study the execution barriers, success factors, and impact assessment of these strategic plans. This will provide an even more holistic understanding of strategic management in the complex, technology-driven future of academic librarianship.

[Purpose/Significance] Disruptive technologies are a core force reshaping the industrial landscape, but their inherent market uncertainty contradicts traditional management logic, posing a significant challenge to the resource allocation decisions of innovation entities. To address this issue, this study, starting from the market characteristics of disruptive technologies, utilizes a hierarchical analysis framework and combines deep learning methods to identify multidimensional market demand themes for potential disruptive technologies and conduct evolutionary analysis. This aims to provide a reliable basis for strategic decision-making and resource allocation by various innovation entities, moving from "experience and intuition" to "scientific foresight." [Method/Process] Based on the substitutive market characteristics of disruptive technologies, a hierarchical analysis framework of "substitutability assessment - multi-entity demand mining - deep clustering" was constructed to identify and analyze multi-dimensional demand market themes based on potential disruptive technologies. First, the set of potential disruptive technologies that has been widely defined in existing research was systematically reviewed. Based on this, an innovation diffusion model was used to quantitatively assess their market substitutability, thereby identifying disruptive technologies with market substitution potential. Secondly, based on the identified technologies with market substitutability, and considering the demand-driven, technology transfer, and institutional guarantee mechanisms for disruptive technology market applications, this study explores multi-dimensional demand content from multiple perspectives, including users, enterprises, and government. It integrates various deep learning methods, such as user demand analysis based on multi-dimensional feature fusion, enterprise demand analysis based on text similarity networks, and government demand analysis based on data augmentation, to differentiate and mine multi-dimensional demand content. Finally, based on the mined multi-dimensional demand content, deep clustering was used to identify core market demand themes for disruptive technologies from multi-source data from users, enterprises, and government, and to analyze their dynamic evolution patterns. [Results/Conclusions] Taking the field of artificial intelligence as an example, this empirical study identified 30 potential disruptive technology market demand themes for 2021-2025, covering global digital trade technology, online behavior governance technology, intelligent waste sorting technology, intelligent transportation technology, intelligent voice interaction technology, digital cultural tourism technology, green technology innovation, green city construction technology, and smart logistics technology. The identified results have been verified by global policy documents and expert authorities, and are highly consistent with the development trends of potential disruptive technologies, effectively echoing the core directions of the current national science and technology innovation strategy and industrial transformation and upgrading. However, this study only focuses on the field of artificial intelligence and does not comprehensively cover different technological fields. Future work will extend to other technological fields to test and improve the general theory of identifying disruptive technology market themes.

Purpose/Significance University libraries are experiencing a structural change in reference and consultation services: user inquiries are increasingly frequent, fragmented, and cross-disciplinary, while service expectations emphasize immediacy, continuity, and actionable guidance. Under such conditions, large language models may relieve routine workloads and extend service availability, yet their library-specific reliability hinges on whether their responses are grounded in local rules, licensed resources, and auditable evidence. This study examines the deployment of DeepSeek in intelligent Q&A and service consultation in university libraries, with two goals: 1) to measure its performance across consultation scenarios, disciplinary domains, and inquiry types; and 2) to clarify the mechanisms that explain why effectiveness improves in some settings but not in others. The study differentiates itself from prior discussions by moving beyond general "potential and risk" arguments to a structured evaluation and mechanism-oriented analysis that links local knowledge governance, scenario engineering, human - machine collaboration, and operational constraints to observable service outcomes. Method/Process The research adopts a mixed-method design and investigates five university libraries in Henan Province that have piloted or deployed DeepSeek-related services to varying degrees. Data sources include a) user questionnaires capturing usage frequency, task preferences, satisfaction, and perceived value; b) staff questionnaires documenting deployment modes, knowledge-base connections, maintenance routines, quality control practices, and operational challenges; c) in-depth interviews that detail workflow design, escalation rules, and the division of labor between librarians and the system; and d) case materials and system records used for triangulation. In total, 850 questionnaires were distributed and 783 valid responses were collected (625 users and 158 staff). An evaluation framework was constructed along four dimensions - technical performance, service effectiveness, user experience, and managerial benefits - to ensure comparability across libraries. Quantitative analyses include descriptive statistics and group comparisons across inquiry types and disciplines, supplemented by mechanism-oriented interpretation using indicators such as the depth of local knowledge integration, the effectiveness of retrieval augmentation, the degree of scenario customization, and the intensity of governance constraints. Qualitative coding of interviews and case materials was conducted to explain observed differences and to identify operational conditions that enable sustained improvement. [Results/ Conclusions Results show that DeepSeek performs well in routine, rule-based consultations. It substantially improves response timeliness and expands service availability, with an overall satisfaction rate of 86.7%. Deep integration with local library knowledge bases is associated with a marked increase in accuracy for library-specific questions (from 64.3% to 93.7%), improved precision in professional literature recommendations (by 35.2%), and higher efficiency in handling complex academic consultations (by 43.8%). However, effectiveness varies systematically: outcomes are better in science and engineering domains and in factual inquiries than in humanities and social sciences and in research- or innovation-oriented inquiries that require domain judgment and verifiable evidence chains. Mechanism analysis indicates that reliability gains depend on 1) robust local knowledge governance with version control and evidence-first retrieval, 2) scenario-specific templates and graded escalation procedures that standardize outputs by task type, 3) human-machine collaboration that supports librarian review, structured correction, and "write-back" updates, and 4) feedback-driven iteration supported by monitoring metrics and accountable operations. The study also acknowledges limitations: the sample is regionally bounded and may overrepresent early adopters; several measures rely on self-reports and short observation windows; and causal identification is constrained by cross-sectional design and rapid model/version iteration. Future research should expand to multi-region samples, incorporate longer-term operational logs, and employ quasi-experimental designs to strengthen causal inference while addressing privacy, compliance auditing, and sustainable governance in library AI services.

[Purpose/Significance] As part of the Healthy China strategy, public libraries are expected to serve as accessible hubs for health knowledge dissemination and community health literacy improvement. However, health information services are inherently high stakes: users' perceived credibility does not necessarily guarantee medical accuracy, and outdated or poorly contextualized information may lead to serious consequences. Evaluations in existing library settings still rely heavily on surveys and experience-based summaries. While these are useful for describing satisfaction, they are often insufficient for identifying process bottlenecks, explaining why specific dimensions perform poorly, or supporting the accountable governance of high-risk content. This study aims to develop an evidence-oriented and interpretable evaluation approach for public library health information services, and to demonstrate how DeepSeek can enhance the measurability and actionability of quality assessment beyond conventional questionnaire-and-interview approaches. [Method/Process] We selected 32 public libraries in Shanxi Province for our empirical study. A four-dimension, sixteen-indicator framework was established (Professionalism, Ease of Use, Timeliness, and Personalization) through literature consolidation, service-process decomposition, and expert consultation. Multi-source data were collected and aligned, including 1 842 valid questionnaires, approximately 2.4 million service log records, and about 5.6 million platform behavior records. DeepSeek was employed to transform unstructured service texts (online consultations, user feedback, activity reviews, and staff response summaries) into structured evidence: it consolidates theme–intent patterns, extracts reason-oriented points that explain dissatisfaction, and maps those textual cues to specific secondary indicators. In parallel, process features such as zero-result search rate, path length, response time, and repeated consultation rate were derived from logs to represent observable friction and efficiency. An intervention validation was conducted using a quasi-experimental pre-post design with matched experimental and control libraries over six months, applying evidence-driven improvements linked to low-performing indicators. [Results/Conclusions] The overall mean score of health information service quality was 3.43 out of 5, indicating a medium level across the sample. Structural analysis shows that professionalism and ease of use exert stronger effects on overall quality, implying that trustworthy content governance and accessible service pathways form the foundational layer of health information services. Personalization receives the lowest average score and the largest between-library variation, suggesting that differentiated entry design and feedback closure mechanisms are the main sources of divergence. Update frequency is positively associated with satisfaction, highlighting the practical importance of routine maintenance rather than campaign-style updates. In the intervention study, the experimental group achieved a 17.5% improvement in overall quality, significantly higher than the control group. The findings indicate that aligning perceived evaluations with behavioral evidence and text-based explanatory cues can improve both interpretability and implementability of quality assessment. Limitations include regional sampling and potential differences in logging completeness. Future work should expand cross-regional validation and incorporate outcome-oriented indicators that more accurately reflect problem resolution.

[Purpose/Significance] This study aims to reveal the influencing factors that affect users' behavioral intention to adopt Artificial Intelligence (AI) smart services in public libraries. As public cultural institutions transition toward intelligent service paradigms, the integration of generative AI offers unprecedented opportunities to enhance knowledge accessibility and operational efficiency. By exploring users' actual needs for AI-driven tools - such as intelligent reference desks, personalized reading recommendations, and automated retrieval systems - this research seeks to provide robust theoretical and practical guidance. Ultimately, it aims to promote the deep integration of AI technologies within the broader framework of smart library construction, ensuring that these innovations align with user expectations and the public interest. [Method/Process] Drawing upon the Technology Acceptance Model (TAM) as the foundational theoretical framework, this study introduces Trust and Perceived Risk as critical external variables to accurately reflect the current technological climate, which is increasingly characterized by data privacy concerns and algorithmic opacity. Data were collected through a structured online questionnaire survey targeting a diverse demographic of public library users, resulting in 257 valid responses. To empirically test the proposed research model and hypotheses, Partial Least Squares Structural Equation Modeling (PLS-SEM) was employed. The rigorous analytical process included a comprehensive assessment of the measurement model to confirm internal consistency, convergent validity, and discriminant validity, followed by the evaluation of the structural model to determine the statistical significance of the path coefficients and the overall explanatory power of the integrated framework. [Results/Conclusions] The empirical evaluation of the structural model yielded several key findings. First, both Perceived Usefulness (PU) and Perceived Ease of Use (PEOU) exert a significant positive impact on user satisfaction, highlighting that functional utility and intuitive interfaces are baseline requirements for AI adoption. Second, Trust, Satisfaction, PU, and PEOU are all identified as strong, direct positive predictors of users' Behavioral Intention (BI) to use AI smart services. Third, Perceived Risk (PR) significantly and negatively influences BI, acting as a major barrier to adoption. Interestingly, the influence of PR on PU was found to be statistically insignificant, suggesting that users evaluate the functional benefits of AI independently of its potential risks. Finally, Trust was shown to effectively mitigate user concerns, exerting a significant negative impact on PR. Based on these insights, it is recommended that public libraries prioritize enhancing the algorithmic transparency of their AI applications to systematically build user trust. Furthermore, libraries should integrate regional cultural elements to develop localized and distinctive AI services, diversify AI application scenarios to meet multifaceted user demands, and actively implement educational workshops and lectures focused on improving public AI literacy.

[Purpose/Significance] Driven by a new wave of technological innovation and industrial restructuring, the construction of smart libraries has become essential for promoting high-quality development and the preservation of public culture. It is crucial to clarify the structural characteristics and evolutionary patterns of their technological innovation in order to realize library digital transformation and achieve the strategy of building a culturally strong nation. Method/Process] Using smart library technology patents in China as the data source, this study constructs a systematic analytical framework of "data-driven → structural analysis → pattern exploration → problem and countermeasure." This study uses methods such as patentometrics, latent Dirichlet allocation (LDA) topic modeling, and logistic curves to delve into the structural characteristics of four dimensions - time, space, subject, and theme - and trace their evolutionary trajectory. [Results/Conclusions] The study reveals the structural characteristics of technological innovation in China's smart libraries comprehensively. The innovation process exhibits distinct "phased leap" features. It has gone through four stages - embryonic, slow development, rapid development, and gradual maturity - and has now entered a period of stock optimization. The spatial distribution presents an unbalanced pattern characterized by "density in the east and sparsity in the west." Although technology diffusion has begun spreading from eastern coastal areas to central and western regions, this fundamental pattern has not yet been substantially altered. Regarding the innovation subject structure, a distinctive "dual-core drive" model has emerged, with universities and enterprises serving as primary innovation engines. However, the collaboration network exhibits a notable characteristic of "strong individual subjects but weak network connections." In terms of technological structure, four main thematic clusters have been identified: smart identification and positioning, data processing and transmission, information retrieval and service interaction, and smart facilities with system collaboration. These four clusters constitute a mature full-chain technological system supporting comprehensive smart library development. Furthermore, the study identifies several key evolutionary patterns. The lifecycle follows an S-shaped growth curve, with projections indicating technology will approach saturation around 2029. The spatial pattern has evolved from initial concentration in eastern regions to gradual diffusion westward. Innovation subject evolution exhibits "peak-and-valley alternation," with complementary oscillations between university basic research and enterprise application transformation. Technological theme evolution reveals four representative paths, collectively demonstrating a progressive logic of "perception → connectivity → intelligence → service." Finally, three major structural bottlenecks are diagnosed: persistent spatial imbalance with impeded diffusion mechanisms; weak collaborative ecosystems among innovation subjects; and lagging integration of cutting-edge technologies. In response, this study proposes targeted countermeasures, including optimizing technological innovation layout, strengthening multi-subject collaboration, and encouraging exploration of high-end technologies. These insights provide valuable references for promoting breakthroughs in smart library technological innovation, accelerating industry digital transformation, and improving the modernization of China's public cultural service system.

[Purpose/Significance] In the contemporary era, which is characterized by digital intelligence, the profound and unprecedented integration of big data analytics and artificial intelligence technologies is fundamentally transforming archival resources. This paradigm-shifting transition transforms traditional, static analog archival records into highly dynamic, interconnected, data-centric digital records. The fundamental transformation of these primary archival management objects triggers an inevitable, multi-dimensional, holistic reconstruction of the prevailing disciplinary paradigm. Consequently, the rigorous conceptualization and structural construction of the "archival data intelligence paradigm" has definitively emerged as a paramount and indispensable research issue for the sustainable development and continuous modernization of archival science. This issue demands meticulous academic scrutiny and strategic foresight. [Method/Process] Grounded firmly in the foundational scientific paradigm theory and meticulously combined with the pre-existing theoretical frameworks of the archival science paradigm, this comprehensive study methodically explores and elucidates the evolutionary lineage and underlying historical logic of the discipline. Through rigorous systematic literature reviews, advanced conceptual model construction, and cross-disciplinary theoretical integration, this research systematically traces the epistemological and methodological trajectory of the archival science paradigm. It provides a critical analysis of the transformative journey from the paradigm of traditional archival documents and historical materials, to the transitional paradigm of archival information resources, and finally to the cutting-edge, technology-driven paradigm of archival data intelligence. [Results/Conclusions] The newly articulated archival data intelligence paradigm intrinsically positions archival digital intelligence as its fundamental operational core. It is powerfully propelled by the dual interactive engines of data element empowerment and artificial intelligence augmentation. Within this sophisticated architectural framework, the "smart archive" functions as the central cognitive brain, meticulously driven by robust computing power hubs and advanced algorithmic machine-learning models, while being reliably supported by the continuous curation of high-quality, machine-readable archival datasets. This synergistic integration fundamentally culminates in a comprehensive theoretical knowledge framework and a robust practical development system, seamlessly synthesizing digital intelligence culture, innovative technologies, specialized talent cultivation, and adaptive administrative management strategies. Furthermore, by rigorously deconstructing the complex theoretical implications of this novel paradigm and firmly grounding it in both profound philosophical foundations and empirical practical development, this study systematically delineates a holistic structural landscape of the archival data intelligence paradigm. This conceptual landscape is mapped out meticulously across five critical dimensions: ontology (redefining the nature of archival data), epistemology (reconceptualizing how archival knowledge is validated), methodology (innovating analytical approaches), technology (implementing advanced AI architectures), and axiology (reevaluating the inherent societal value of archives). Ultimately, this comprehensive theoretical construct provides invaluable conceptual references and strategic, practical guidance. It actively facilitates the successful digital intelligence transformation and future-proofing of contemporary archival institutions and the broader archival profession worldwide.

[Purpose/Significance] Amid the national push for the high-quality development of the philosophy and social sciences (PSS), organized scientific research is essential for overcoming disciplinary barriers and solving major practical issues. Existing studies mainly focus on natural sciences, ignoring PSS organized research's uniqueness - its heavy reliance on independent thinking and open academic exchange. This study fills this gap by exploring factors and mechanisms influencing PSS researchers' willingness to participate in organized collaborations, enriching the discipline-specific theoretical system and providing targeted references for optimizing institutional design, boosting participation enthusiasm, and enhancing collaborative innovation efficiency. [Method/Process] Guided by social ecosystem theory, a four-dimensional framework (micro-individual, meso-team, external-organizational, macro-institutional) was constructed. A questionnaire survey was conducted among PSS researchers (university faculty, research institute staff, doctoral students), yielding 371 valid samples after excluding flawed responses. Binary Logistic regression identified key influencing factors, while Interpretive Structural Modeling (ISM) analyzed their hierarchical structure and transmission paths. SPSS 27.0 and Amos 29.0 validated the measurement scales, confirming good internal consistency and structural validity. [Results/Conclusions] Binary Logistic regression shows ten factors significantly predict participation willingness. Microscopically, academic rank (marginally significant), research capability fit, and perceived relative advantage (strongest driver) are core individual motivations. Meso-level factors - team knowledge heterogeneity, mission identification, and knowledge integration system - exert the most prominent promotional effects. Externally, institutional and incentive guarantees form a "dual pillar," with strategic resource support marginally significant. Macroscopically, new nationwide system support and interdisciplinary orientation shape the overall environment. ISM reveals a hierarchical transmission mechanism: root factors (national system, interdisciplinary orientation) stimulate external support, foster meso organizational maturity, and act on micro factors, forming a complete chain. Three key paths are identified: institutional drive, evaluation traction, and resource collaboration. Limitations include cross-sectional data (unable to confirm causality) and insufficient interdisciplinary difference analysis. Future research may adopt longitudinal tracking, supplement qualitative methods (case studies, interviews), and conduct interdisciplinary comparisons to enhance conclusion depth.

[Purpose/Significance] With the growing demand for public health literacy and the accelerating digital transformation of libraries, medical science popularization services in libraries are expected not only to disseminate information but also to promote learning, skill acquisition, and behavioral change. However, existing services still rely heavily on text-based explanation, one-way communication, and short-term activity organization, which makes it difficult for users to translate received information into practical health knowledge and sustained health action. From this perspective, the core problem is not simply a lack of content or channels, but the weak connection between medical knowledge, bodily experience, everyday situations, and long-term social support. Drawing on embodied cognition theory, this study introduces the metaverse as a possible service environment for reconstructing library-based medical science popularization. The main innovation of this study lies in shifting the analytical focus from media form to cognitive mechanism, and in proposing a framework that connects scenario construction, multisensory interaction, collaborative participation, and service evaluation. This framework is expected to enrich the theoretical discussion of library health science communication and provide an operable path for the upgrading of medical-themed science popularization services. [Method/Process] This study adopts a qualitative and conceptual research design that combines literature review, theoretical analysis, and case-based interpretation. First, previous studies on library health information services, medical science communication, user adaptation, digital health literacy, and immersive technologies were reviewed in order to identify the major problems of current medical science popularization services in libraries. Second, embodied cognition was used as the core theoretical lens to extract three key dimensions, namely hybrid physical-virtual space, multisensory interaction, and collaborative community network. Based on these dimensions, the study constructs a metaverse-based service framework and explains how medical knowledge can shift from abstract presentation to contextualized understanding, embodied rehearsal, and behavioral reinforcement. Third, an immersive interactive exhibition on myopia prevention was selected as an illustrative case. The case is not used as strict empirical verification, but as a representative scenario through which the proposed framework can be mapped onto concrete design elements, including space organization, positional interaction, dynamic rendering, experience guidance, and the possibility of extension toward routine library services. This method is appropriate because the research topic is still in an exploratory stage, real-world library cases remain scattered, and conceptual clarification is necessary before controlled empirical testing and large-scale implementation can be meaningfully developed. [Results/Conclusions] The study identified three closely related bottlenecks that current library-based medical science popularization services face. First, knowledge is often detached from real-life situations. This means that users may understand medical terms superficially but still fail to apply them to concrete health decisions. Second, interaction is often limited to reading, listening, or watching, while repeated practice, correction, and embodied rehearsal are insufficient, making it difficult to internalize operational knowledge. Third, many existing services remain event-oriented and discontinuous, lacking stable support structures that connect librarians, medical professionals, users, families, schools, and communities. In response, this study proposes a metaverse construction scheme centered on three modules. The first is hybrid physical-virtual space, which organizes high-frequency health issues into explorable scenarios and links physical library space with digital simulation environments. The second is a multisensory interaction system that transforms medical concepts into visible, audible, touch-responsive, and action-related experiences, thereby strengthening comprehension through perception-action coupling. The third is a collaborative community network that extends science popularization beyond one-time events by incorporating expert consultation, peer support, family co-learning, and community participation. These three modules are integrated through a closed-loop operational logic of immersion, interaction, feedback, and adjustment. On this basis, the study further proposes implementation strategies concerning user segmentation, multimodal resource integration, platform construction, and a multidimensional evaluation mechanism covering participation, knowledge acquisition, behavioral conversion, and experience-based trust.

[Purpose/Significance] Research libraries constitute a vital component of China's science and technology (S&T) think tank ecosystem. Their intelligence services for government departments for S&T project performance evaluations can enhance the scientific rigor and accuracy of the outcomes while catalyzing their own diversified transformation and development. Existing research has demonstrated that research libraries can effectively support government departments in delivering intelligence services such as S&T project performance evaluation. However, current research predominantly focuses on holistic studies of intelligence services undertaken by research libraries, with a notable deficiency in targeted investigations specifically addressing their engagement in S&T project performance evaluation services. Anchored in the empowerment of multi-source data, this study focuses on exploring service models for the evaluation of S&T project performance in research libraries. It examines the potential of these services for government departments conducting multi-source data-empowered S&T project performance evaluation. Additionally, the study promotes the enrichment of vertical application scenarios in library and information science-related fields and supports the construction of digital and intelligent systems for S&T evaluation. [Method/Process] Through literature review, web-based investigation, and information correlation methods, this study systematically examines typical domestic and international practices of multi-source data-empowered S&T project performance evaluation, analyzes the alignment between such practices and research libraries' capabilities, and elucidates the significance of research libraries' engagement in S&T project performance evaluation. Based on the typology and characteristics of multi-source data, and leveraging the inherent advantages of research libraries, we clarify the evaluation rationale and principles, and construct a logical framework and innovative model for multi-source data-driven S&T project performance evaluation. [Results/Conclusions] Typical countries and regions both domestically and internationally emphasize the flexible application of multi-source data in project performance evaluation, establishing management platforms that aggregate full-lifecycle project data, and underscoring inter-departmental collaboration and data linkage among multiple government agencies. As neutral third-party institutions and critical data hubs, research libraries, when conducting S&T project performance evaluation under the new era context, are characterized by their emphasis on integrating fiscal fund effectiveness, S&T policy implementation efficacy, modernized governance systems, and multi-source S&T project data. They can construct innovative models for S&T project evaluation across four dimensions: data infrastructure development, indicator system construction, performance analysis and evaluation, and evaluation results application - thereby supporting the formation of a complete closed-loop chain of "evaluation-feedback-correction-improvement" for S&T project performance evaluation. Furthermore, this study proposes strategic recommendations for research libraries to broaden vertical application scenarios of S&T intelligence and deepen services for S&T project performance evaluation. These suggestions are listed as follows: strengthening policy intelligence service capabilities to enhance decision-support levels; deepening data resource integration and sharing to improve data support capacity; and strengthening the development of interdisciplinary talent teams to advance intelligence service quality. Future research directions will involve conducting specific case study analyses to provide support for refining the model of research library services for S&T project performance evaluation.

[Purpose/Significance] In the context of the "Healthy China 2030" strategy and the rapid advancement of medical technology, the innovative capacity of medical postgraduates is pivotal in driving clinical breakthroughs and translating medical research into practice. This study focuses on medical postgraduates, exploring the complex causal relationships between various dimensions of information literacy and innovation ability from an information science perspective. The goal is to provide empirical evidence that can enhance the innovation capacity of medical postgraduates. [Method/Process] A mixed-methods research design was employed. First, a questionnaire survey was conducted with 368 medical postgraduates from a university in Hainan, China, measuring four dimensions of information literacy (information awareness, knowledge, capability, and ethics) and innovation ability. Fuzzy-set qualitative comparative analysis (fsQCA) was then used to identify the pathways through which combinations of these dimensions lead to high or low levels of innovation ability. To supplement and validate the quantitative findings, semi-structured interviews were conducted with 20 postgraduates, representing varying levels of innovation ability, to provide deeper insights. [Results/Conclusions] The fsQCA revealed a complex causal structure, identifying four configurations leading to high innovation ability (overall consistency: 0.835; coverage 0.702) and three configurations associated with low innovation ability (overall consistency: 0.913; coverage 0.658). The results show that no single dimension is a necessary condition for high innovation. Instead, multiple equally effective pathways exist. Information capability emerged as a core condition in three of the four high-level configurations, with a necessity consistency of 0.802, highlighting its foundational role as an "approximate necessary condition." The configurations also revealed significant synergistic effects. For instance, one pathway (Configuration 1: high information awareness and capability compensating for low knowledge) demonstrates that strong awareness and practical skills can offset gaps in theoretical knowledge, often facilitated by AI tools. Interview data reinforced these findings: high-innovation postgraduates emphasized the importance of information capability in efficiently synthesizing evidence, while those with low innovation identified weak information awareness (e.g., insensitivity to research frontiers) and limited information capability as primary barriers. The study also identified three distinct pathways to low innovation, characterized by the absence of key dimensions, such as awareness, knowledge, and capability, occasionally compounded by ethical lapses. The study concludes that fostering innovation among medical postgraduates requires shifting from a one-size-fits-all approach to a configuration-oriented support system. By combining quantitative pathways with qualitative insights, universities can develop tailored, multi-layered information literacy programs. This study is limited by its single-institution sample and cross-sectional design. The identified pathways may be context-specific and do not capture the dynamic evolution of information literacy configurations over time. Future research should expand to multi-center studies across diverse institutional contexts. We will use longitudinal designs to examine how configurations change over time. Additionally, we will explore how factors such as supervisory style and resource availability influence these pathways.

[Purpose/Significance] To promote the development of world-class scientific journals, it is necessary for journals to enhance their understanding of the evolving trends in scientific frontiers and research hotspots. Therefore, characterizing the participation and functional role of journals in research hotspots from the perspective of the journals themselves is of great significance for advancingg journal evaluation theory and promoting discipline development, and journal construction. Relatively little existing research has addressed the role of journals in forming research hotspots within disciplines. It is difficult to reveal the functional differences between journals in the formation and evolution of research hotspots. This study proposed a journal hotspot index evaluation and method based on highly cited papers, revealing the role and function of academic journals in research hotspots in the field, promoting the diversified evaluation of academic journals, and providing a new perspective and useful supplement to journal evaluation. [Methods/Process] Highly cited papers are often concentrated on research topics that have received high attention and continuous discussion in the academic community within a specific period. Their concentrated appearance usually corresponds to the formation of research hotspots or important theoretical progress. Based on the participation and citation of highly cited papers by journals, this study constructed two measurement indicators: the journal's hotspot leadership and hotspot absorption. Based on this, a quadrant diagram was established to classify and evaluate four different types of journal sets, revealing their distribution characteristics and patterns. This study used ESI agricultural science journals as an example to explore the correlation between the journal hotspot index and traditional journal metrics. [Results/Conclusions] The journal hotspot index demonstrates good discriminatory and identifiable characteristics, complementing existing journal evaluation metrics to some extent and identifying journals playing a key role in the development of research hotspots in the field. As a valuable supplement to the journal evaluation index system, the journal hotspot index offers a new analytical perspective on journal development and quality levels, facilitating a more comprehensive and accurate evaluation and positioning of journals, and providing a reference for the development and construction direction of journals in my country. However, the index system still has certain limitations, mainly in the time window for highly cited papers and its adaptability to different subject areas. Future research could expand into more subject areas to further verify the interdisciplinary applicability of this method. Furthermore, we will consider introducing more dynamic window mechanisms to enhance the index's adaptability to emerging disciplines and journals, thereby improving the universality and explanatory power of the journal hotspot index.

[Purpose/Significance] With the expansion of user-generated content (UGC) communities, users have increasingly engaged in interaction behaviors that go beyond passive consumption. Among these behaviors, urging creators to update content has emerged as a distinctive form of user participation. Unlike conventional feedback behaviors that respond to existing content, urging behavior is future-oriented and directly intervenes in the content production process, reflecting users' expectations, motivations, and relational orientations toward creators. Despite its growing visibility, research in the field of library and information science (LIS) rarely examines urging behavior as an independent object of analysis. Theoretical investigation of its behavioral logic and formation mechanisms remains deficient. This study aims to systematically explore how urging behavior emerges and evolves, and how it is shaped by multiple influencing factors. This will contribute to a deeper understanding of how users interact with information in UGC communities. [Method/Process] This study adopts the grounded theory approach, conducting in-depth interviews with 20 active users in the UGC communities. Data were collected and analyzed using Nvivo11 software through a three-level coding process, including open coding, axial coding, and selective coding. [Results/Conclusions] The study constructs two interrelated analytical models. First, a dynamic behavioral pathway model is developed, revealing urging behavior as a cyclical process. The process is initially triggered by either cognitive motivations or emotional motivations. These motivations drive users to engage in three distinct types of urging behavior: semantic expression-based urging, quick operation-based urging, and economic incentive-based urging. Users then receive positive or negative feedback from creators or the community, which leads them to conduct subjective evaluations of their urging experience in terms of emotional responses and perceived effectiveness. These evaluations subsequently influence users' future motivations, forming a continuous feedback loop. Second, a comprehensive influencing-factor model is established, identifying five categories of core factors: user characteristics, content attributes, creator attributes, platform characteristics, and situational factors. The study further clarifies the complex interactions among these factors. User-related factors and content-related factors exert direct effects on urging behavior. Content factors and creator factors also generate indirect effects through the mediating role of user factors. In contrast, platform characteristics and situational factors function as moderating variables that shape the strength of the relationship between user factors and urging behavior. Together, these findings provide a nuanced explanation of why and how users engage in urging behavior. The study discusses the theoretical contributions of integrating the behavioral pathway model with the influencing-factor model and offers preliminary practical implications for platform governance and creator strategies. Limitations include the qualitative nature of the study and the relatively small sample size. Future research is encouraged to conduct quantitative validation and cross-platform comparative analyses to further extend these findings.

[Purpose/Significance] Against the backdrop of data becoming a key factor of production, the sharing and utilization of scientific data face significant challenges, including "market failure" and a fragmented policy landscape. Existing academic efforts often analyze policies from an isolated perspective. These efforts lack a holistic framework to understand how policies interact with multiple stakeholders to create value from data. This study aims to address this gap by constructing an integrated analytical framework for scientific data sharing policies. Its primary significance lies in providing a systematic tool to deconstruct policy architecture, dynamically reveal the internal transmission mechanism from policy intervention to value realization, and offer evidence-based insights for optimizing top-level design. This contributes to building a more efficient data governance ecosystem, ultimately enhancing the allocation efficiency of scientific resources and national innovation capacity. [Methods/Process] The research employs a mixed-method approach combining theoretical construction and empirical text analysis. Firstly, through a synthesis of literature on policy instruments, stakeholder theory, and data factorisation, a three-dimensional analytical framework encompassing "Policy Instruments, Stakeholders, and Factorisation Stages" was constructed. To animate this static structure, the Stimulus-Organism-Response (SOR) model was introduced as an overarching theoretical lens, formulating a "policy stimulus (S) → stakeholder perception/organism (O) → factorisation response (R)" dynamic mechanism. Secondly, to empirically apply and validate the framework, representative policy documents, including the national "Measures for the Management of Scientific Data" and selected local implementation rules, were chosen as cases. Using qualitative data analysis software NVivo 12, 174 relevant policy clauses were extracted. A rigorous coding process based on the three-dimensional framework was conducted independently by two researchers to ensure reliability. The inter-coder consistency was measured with Cohen's Kappa coefficient, yielding a result of 0.82, which indicates almost perfect agreement. Discrepancies were resolved through discussion and expert consultation. Finally, statistical analysis was performed on the coded data to quantify the distribution of policy attention and identify characteristic patterns. [Results/Conclusions] The study yields three sets of core findings. First, it conceptualizes the factorisation of scientific data as a three-stage transition: "Digitization" (transforming raw information into structured data), "Valorization" (enhancing data into valuable assets through processing), and "Sharization" (releasing multiplied value through circulation and reuse). Second, the quantitative analysis reveals a distinct imbalance in current policy attention allocation. Regarding policy instruments, emphasis is heavily skewed towards "Planning & Organization" (35.63%) and "Sharing & Reuse" (21.84%), while the crucial intermediate stage of "Storage & Publication" is under-supported (10.34%). Concerning stakeholders, "Sharers" (e.g., researchers) are the central focus (43.10%), whereas "Intermediators" (e.g., data centers) are relatively marginalized (23.56%). In terms of factorisation goals, policies overwhelmingly prioritize the final "Sharization" stage (71.84%), overlooking the foundational "Digitization" and "Valorization" stages. Third, the research identifies several synergistic and effective policy pathways, such as "Mandatory Submission + Standard Constraints" and "Data Processing + Talent Incentives". Based on these conclusions, the study proposes that future policy optimization should focus on rebalancing attention towards intermediate processes and intermediary actors, strengthening whole-lifecycle governance, and enhancing the synergy of policy tools. Exploring innovative governance models like data trusts is also recommended to foster a sustainable data-sharing ecosystem. A main limitation of this study is its reliance on textual analysis; future research could employ surveys or interviews to empirically validate the SOR mechanism by measuring stakeholders' actual perceptions and behavioral responses, and test the framework's applicability in other specific data domains.

[Purpose/Significance] Amid rising concerns over the commercialization of scholarly publishing and the financial burden of APC-based models and transformative agreements, diamond open access (Diamond OA) has gained attention as a non-profit, community-governed alternative. Current open science debates increasingly emphasize a shift from improving access to transforming the governance of knowledge production, often termed as "community over commercialization." In this context, Diamond OA is not merely a cost-free publishing option but a governance paradigm in which academic communities organize and sustain scholarly communication. This study positions Diamond OA within international discussions on open infrastructure, bibliodiversity, and equitable knowledge systems, and examines how its community-driven logic shapes goal setting, operational mechanisms, and evolutionary trends. It also explores how this governance logic generates structural tensions related to funding sustainability, infrastructural fragmentation, and evaluation regimes, with particular attention to implications for China. [Method/Process] The study employs a qualitative multi-method design integrating literature review, cross-regional case comparison, institutional analysis, and SWOT assessment. An analytical framework of "goal system - operational mechanisms - structural challenges - localization pathway" has been constructed to examine representative Diamond OA practices. Cases including SciELO, Redalyc, the Open Library of Humanities (OLH), and the Public Knowledge Project (PKP) are analyzed to identify four organizational archetypes: national or regional alliances, scholar-led community governance, technology-empowered infrastructures, and overlay publishing models. These cases illustrate how consensus decision-making, pooled resource governance, collaborative feedback, and trust-based quality control function as core operational mechanisms. The SWOT analysis further reveals the dynamic interaction between internal characteristics and external environmental conditions. [Results/Conclusions] The findings indicate that Diamond OA reorganizes scholarly publishing around community trust, shared responsibility, and public-interest orientation. It enables practices such as multilingual publishing, open peer review, and greater participation from non-English-speaking regions, thereby enhancing bibliodiversity and academic visibility. However, the model faces persistent structural constraints, including unstable funding, uneven technical capacity, and marginalization within evaluation systems dominated by commercial metrics. These challenges stem directly from its non-commercial and community-dependent nature. Internationally, Diamond OA initiatives show trends toward more structured governance networks, interoperable open infrastructures, and cross-regional collaboration. In China, despite advances in open science policy and infrastructure, Diamond OA development remains fragmented, with unclear community roles and rigid evaluation constraints. Rather than replicating international models, this study proposes a localized "four-wheel drive" framework - policy coordination, community governance, infrastructural empowerment, and evaluation reform - to integrate Diamond OA into China's scholarly communication system. This framework contributes to global discussions by demonstrating how community governance can be adapted within a state-coordinated context and offers practical guidance for developing sustainable and equitable open access practices.

[Purpose/Significance] The rise of Generative Artificial Intelligence (AIGC) has made "prompt literacy" a crucial skill for effective human-AI interaction. However, there are significant gaps in public competency that risk widening the digital divides Libraries, as foundational institutions for literacy and access, are ideally positioned to address this need. This study aims to clearly define the core roles of libraries in cultivating public prompt literacy and to develop a practical, actionable framework to guide their efforts in the AIGC era, thereby enhancing their social relevance and service impact. [Method/Process] This research employs a qualitative, multi-stage approach. First, a comprehensive literature review was conducted to analyze and synthesize the theoretical conception and multi-dimensional structure of prompt literacy. Second, through a strategic analysis of libraries' inherent functions and societal mandates, the study systematically proposes a tripartite role orientation. Third, building on this role definition, an integrated practical framework was constructed. This framework synthesizes insights from library science, educational design, and technology ethics, and is informed by an examination of early innovative practices from libraries globally, moving from conceptual roles to actionable strategies. [Results/Conclusions] The study concludes that to effectively foster public prompt literacy, libraries must consciously adopt and integrate three core roles. First, as an educational guide, libraries must transition from information providers to facilitators of critical thinking and technical skill-building, specifically in human-AI collaboration. Second, as technology adapters, they must act as crucial intermediaries, assessing, curating, and sometimes tailoring AI tools to lower access barriers and meet diverse user needs. Third, as an ethical guardian, they have a responsibility to navigate the risks associated with AIGC, such as misinformation, bias, and privacy concerns, thereby fostering a trustworthy information environment. From this integrated role orientation, a detailed four-dimensional practical path is formulated. 1) Resource construction involves building a multi-layered support system, including a repository of reusable prompt templates for common and discipline-specific tasks, as well as educational materials highlighting ethical pitfalls and case studies. 2) A hierarchical education system requires the design and delivery of differentiated instructional programs. These programs range from gamified workshops for youth and students, to advanced, discipline-integrated training for researchers and professionals, and from patient, needs-based, low-barrier tutorials for seniors to programs for the digitally disadvantaged. 3) Service integration emphasizes the importance of seamlessly embedding prompt literacy support into core library services and user workflows. This includes integrating prompt design assistance into research consultations, embedding literacy modules into academic course curricula in partnership with faculty, and demonstrating AIGC applications in everyday life through community programs. 4) Ethical regulation requires the operationalization of ethical principles through explicit policies for library AI use, transparent communication with users about AI-assisted services, the development of ethical checklists and assessment tools, and the fostering of community dialogue on AI ethics. This comprehensive framework gives libraries a strategic roadmap for translating the importance of early prompt literacy development into practical, long-lasting, services. Implementing this approach allows libraries to strengthen their public education mission in the digital age, establish themselves as vital and adaptable community hubs, and play a pivotal role in fostering a more literate, equitable, and ethically conscious society amid rapid AI advancements. Future research could focus on assessing the impact of these interventions and identifying the skills necessary for librarians to fulfill these new roles successfully.

[Purpose/Significance] Disruptive technology identification has become an increasingly important research topic in the context of rapid technological evolution and strategic decision-making for governments and enterprises. However, existing data-driven identification approaches often suffer from two critical limitations. First, disruptive technology datasets are typically characterized by severe class imbalance, where truly disruptive cases constitute only a small fraction of the total samples, leading to biased learning and poor generalization. Second, most existing studies rely on a single machine learning model, which limits the ability to capture complex and heterogeneous patterns embedded in high-dimensional technical text features. These issues restrict the robustness, accuracy, and practical applicability of current identification frameworks. To address these challenges, this study aims to construct an optimized disruptive technology identification model that jointly considers data imbalance mitigation and model performance enhancement, thereby improving the reliability and stability of predictive results and contributing to methodological advancements in technology intelligence and innovation management research. [Method/Process] Based on the reproduction of a widely used baseline model built upon XGBoost, this study proposed a two-stage optimization framework integrating data resampling and ensemble learning. In the data preprocessing stage, a hybrid SMOTE-ENN sampling strategy was employed to reconstruct the training dataset. The SMOTE component synthetically generated minority class samples to enhance class representation, while the ENN component removed ambiguous and noisy samples from overlapping regions, thus achieving a balance between noise reduction and information preservation. This strategy effectively alleviated the adverse impact of class imbalance on model learning without excessively distorting the original data distribution. In the modeling stage, a stacking-based ensemble learning framework was constructed by integrating multiple heterogeneous base learners, including XGBoost, LightGBM, Extra Trees, and Support Vector Machines. These base models were selected to capture complementary decision boundaries and feature interactions from different learning perspectives. A Random Forest model was further employed as a meta-learner to aggregate the outputs of the base learners and perform higher-level feature integration. Through this hierarchical learning mechanism, the proposed framework enhanced both representation capability and predictive robustness, enabling more accurate identification of disruptive technologies under complex and noisy data conditions. [Results/Conclusions] Extensive experimental evaluations demonstrate that the proposed optimization model significantly outperforms the baseline XGBoost model across multiple core performance metrics, including Accuracy, Precision, Recall, and F1-Score. Notably, the F1-Score, which is substantially improved from 0.63 to 0.98, indicates a marked enhancement in the model's ability to correctly identify minority disruptive technology samples while maintaining high overall stability. The results confirm that the combined application of hybrid resampling and ensemble learning effectively addresses the challenges of sample imbalance and model bias in disruptive technology identification tasks. In conclusion, the proposed framework provides a robust and scalable solution for identifying disruptive technologies in high-dimensional, imbalanced data scenarios. Beyond improving prediction accuracy, this study offers methodological insights for technical text modeling and innovation analytics. Its approach can be easily adapted to other fields with similar data imbalance and complexity issues. Future research may further explore adaptive sampling strategies and deep learning-based ensemble architectures to enhance temporal and semantic representation capabilities.

[Purpose/Significance] Against the global surge of generative artificial intelligence (GenAI) and large language models (LLMs), academic libraries are undergoing a critical paradigm shift in their reference services. While "AI Virtual Librarians" (AIVL) are increasingly adopted to enhance efficiency, cross-national evidence regarding how they are configured alongside traditional "Human Live Reference" (HLR) remains scarce. This study aims to reveal the structural differences in human-AI configurations between Chinese and international top-tier university libraries. It seeks to identify the divergence between "technology-driven" and "human-centric" service models and proposes a governance-oriented hybrid pathway to inform the digital transformation of academic libraries. [Method/Process] The study established two high-resource samples: 42 libraries from China's "Double First-Class" universities and 94 libraries from the U.S. News Top 100 World Universities. A systematic website investigation and standardized interaction tests were conducted to collect data on service availability and deployment models. The study not only quantified the deployment of HLR and AIVL (classified into rule-based and LLM-based) but also qualitatively evaluated the "Core Service Contents" and "Linkage Mechanisms" (e.g., traceability, boundaries, and human fallback). Chi-square tests were employed for statistical analysis, and robustness checks were performed using both broad and strict counting rules to ensure validity. [Results/Conclusions] Results indicate that while the overall service coverage is similar across groups (approx. 74%), the service structure diverges significantly. International libraries predominantly rely on the "Human-only" mode (66.0%), prioritizing deep research support, academic integrity, and privacy protection. In contrast, Chinese libraries show a significantly higher adoption of AIVL (57.1% vs. 8.5%) and LLMs (26.2% vs. 1.1%), with 52.4% operating in an "AI-only" mode. Content analysis reveals that Chinese AIVLs focus on transactional efficiency and 24/7 accessibility, whereas international counterparts focus on distinct research guides and governance. The study identifies a critical trade-off: China's aggressive AI adoption enhances accessibility but faces challenges regarding answer hallucinations and the lack of human fallback mechanisms. To address these challenges, the paper recommends a "Human-AI Collaborative Loop" model. Key strategies include: 1) Implementing risk-tiered routing, where low-risk transactional queries are handled by AI and high-risk research inquiries are directed to humans; 2) Optimizing AI reliability through Retrieval-Augmented Generation (RAG) and controlled knowledge bases to ensure traceability; 3) Establishing clear governance boundaries and stratified implementation paths for libraries with different resource levels, ensuring a balance between technological innovation and service ethics.

[Purpose/Significance] With the accelerated convergence of artificial intelligence and the metaverse, smart library information services are undergoing a profound transformation from tool-oriented functional optimization toward holistic cognitive support. Traditional information retrieval and service models increasingly struggle to explain and support complex cognitive activities involving multi-agent collaboration, contextual awareness, and continuous knowledge construction. From the perspective of human-machine-environment collaborative cognition, this study aims to explore the paradigm shift of smart library information services in intelligent digital environments and to establish an integrated theoretical framework that coordinates technological systems, cognitive processes, and contextual factors, thereby providing a systematic theoretical foundation for service model innovation and capability enhancement in smart libraries. [Method/Process] This study first reviews the evolutionary trajectory of information search paradigms - from symbolic computation and semantic understanding to social perception - through systematic literature analysis. We proposed Ecological Search as an emerging paradigm. Drawing on distributed cognition, embodied cognition, and information ecology theories, a human-machine-environment cognitive symbiosis search architecture was constructed, driven by a dual core of social multi-agent communities and contextualized metaverse environments. The architecture operates through an inner-outer dual-loop mechanism consisting of environmental perception and intention emergence, federated retrieval and knowledge fusion, collaborative generation and narrative construction, and cognitive evolution and ecological calibration. Furthermore, an "interaction-knowledge-context" three-dimensional analytical model was developed to decompose key service capabilities and derive differentiated integration pathways under diverse service objectives. [Results/Conclusions] The study proposed three smart library information service models: interaction-enhanced integration, knowledge-reconstructive integration, and context-immersive integration, and clarified how a unified cognitive architecture can be flexibly configured for different user groups and service scenarios. The findings indicate that the ecological search paradigm transcends system-centered instrumental rationality and reconceptualizes information search as a human-machine-environment collaborative process supporting continuous cognitive construction. By integrating multi-agent systems and contextualized environments, this paradigm provides essential mechanisms for smart libraries to move beyond information provision toward advanced cognitive support. The study offers theoretical insights and practical implications for achieving an ecological transformation of smart library information services while balancing technological innovation and human-centered values.

[Purpose/Significance] As digital technologies such as 5G and generative AI become more prevalent in higher education, university libraries have evolved from traditional collections of books to ecosystems of cross-modal and multi-source resources, encompassing core collection resources, open-access resources, and user-generated content. However, the "resource silo" issue caused by heterogeneous resources and the mismatch between passive services and dynamic user scenarios in research and teaching remain unresolved. Existing studies lack integrated closed-loop mechanisms linking resources, scenarios, and users. This study aims to address these gaps by promoting libraries' transformation from "resource storage centers" to "proactive knowledge service centers." Its key innovation lies in constructing a scenario-driven three-dimensional collaborative model, which bridges the disconnect between resource integration and scenario adaptation, providing theoretical and practical support for intelligent library development. [Method/Process] Guided by ERG demand theory and context-aware computing, this study adopts a mixed-methods approach combining literature research, technical design, and case validation. A three-dimensional collaborative model of "Resource Integration - Scenario Adaptation - Smart Services" was proposed. For resource integration, a "three-dimensional integration + four-step fusion" framework was developed: standardized access via unified DCAT-AP/RDA metadata and multi-protocol gateways, associative reorganization through cross-modal semantic matching and knowledge graph aggregation, and hierarchical storage (hot/warm/cold tiers). The four-step fusion includes data preprocessing, modality conversion (ViT, Whisper-large, YOLOv8 models), feature fusion (attention mechanism + Transformer encoder), and knowledge generation (knowledge graphs, rule bases). An innovative five-dimensional dynamic scenario model (S=f(P,R,S,T,C)) quantifies user profiles, resource attributes, spatial locations, temporal contexts, and social connections for precise scenario identification. Technically, a "cloud-edge-device" architecture provides support, while a hierarchical service pathway (instant/in-depth/customized services) and a multi-dimensional evaluation system (resource/service/user dimensions) ensure closed-loop optimization. [Results/Conclusions] The model effectively achieves in-depth integration of multi-source cross-modal resources and precise scenario adaptation. Validated through typical applications - full-cycle research support and immersive teaching (VR ancient book restoration, MR anatomy demonstration) - it significantly enhances resource utilization efficiency and user experience, resolving the core pain point of resource-scenario disconnection. The model strongly supports libraries' transformation from passive resource supply to proactive knowledge services. Limitations include limited application of cross-modal technologies to virtual reality resources, insufficient coverage of management and social service scenarios, and the need for long-term validation of the evaluation system. Future research will deepen large-model-aided cross-modal fusion, expand scenario coverage, improve the evaluation system with third-party participation, and promote inter-university resource sharing to better support higher education development.

[Purpose/Significance] As the core hub for public cultural services and the inclusive dissemination of knowledge, the digital transformation of smart libraries is accelerating continuously. However, they also face multiple digital risks such as data fragmentation, insufficient technological adaptation, and prominent system vulnerabilities, which seriously constrain the stability and sustainability of public cultural services. The construction of digital resilience has become a key support for smart libraries to respond to environmental changes and ensure the realization of core functions. This paper focuses on the sustainable development demands of smart libraries in the digital age. Based on the dual-wheel drive perspective of "data elements-digital technology", it explores the generation logic and improvement path of digital resilience. This approach can not only provide a new dimension for improving the theoretical system of digital risk governance in smart libraries, but also provide practical solutions to solve real problems such as data fragmentation and insufficient technical adaptation. Furethermore, it can enhance the stability and efficiency of public cultural services. [Method/Process] Supported by theories of data governance, technological innovation and organizational resilience, this research adopts a progressive approach of literature review, logical deconstruction, framework construction and path optimization, and integrates literature research methods, system deconstruction methods and logical deduction methods. We systematically analyze the penetration and impact of data elements and digital technologies on the resources, services, technologies, and organizational dimensions of smart libraries, clarify the correlation logic and operational mechanism between dual-wheel drive and digital resilience, construct practical approaches from two aspects: the release of data element value and the collaboration of digital technology clusters, and provide a multi-dimensional guarantee system. [Results/Conclusions] The core essence of digital resilience in smart libraries lies in their dynamic adaptation, efficient response, and continuous evolution capabilities in the face of digital risks. Its formation relies on the deep collaboration between data elements and digital technologies: Data elements, by building a multimodal collaborative data ecosystem, break down information silos and lay a solid resource foundation for digital resilience. Digital technology, relying on the collaborative efforts of technology clusters such as big data, artificial intelligence, and blockchain, has formed a full-cycle risk response technology system covering risk perception, emergency response, and system recovery. The coupled interaction between the two promotes a qualitative leap in digital resilience from passive risk resistance to active value creation, ultimately achieving a deep integration and development driven by data elements - digital technology-driven and resilience construction. Based on this, practical suggestions are put forward. Smart libraries should strengthen the standardized construction of data governance, promote the scenario-based application of technology clusters, and improve the cross-departmental collaboration mechanism.

[Purpose/Significance] Against the backdrop of the digital wave and the Healthy China initiative, efforts to enhance national health information literacy face challenges, including an insufficient supply of high-quality popular science content and low public enthusiasm for its dissemination. This study aims to explore the internal driving forces, core influencing factors, and transmission paths of the willingness to share online health popular science information. It further intends to provide theoretical support for regulatory authorities and popular science platforms in formulating incentive policies and safeguard mechanisms, thereby promoting the participation of social entities in popular science dissemination, increasing the supply of high-quality popular science resources, and enhancing the health information literacy of the general public. [Method/Process] A three-stage research design of "Grounded Theory - Fuzzy DEMATEL - ISM" was adopted. Firstly, interview data from diverse groups were collected through semi-structured interviews. Grounded Theory was then applied to coding to extract initial influencing factors and construct a multi-dimensional driving force system. Secondly, Fuzzy DEMATEL was used to calculate the centrality and causality degrees, so as to identify key factors. Finally, the interpretive structural modeling (ISM) method was employed to integrate the influencing factors, establish a hierarchical structure, and clarify the transmission logic and action mechanism. This method not only enables the acquisition of the most original influencing factor system from interview materials but also reveals the interaction relationships among these factors, which is in line with the research requirements and trends in the field of information science. [Results/Conclusions] The results of Grounded Theory analysis identified 13 influencing factors, which are categorized into four dimensions. The personal dimension includes four factors: interpersonal interaction traits, perceived utility, health information literacy, and self-efficacy. The information dimension consists of four factors: information quality, information source credibility, information richness, and information clarity. The platform dimension comprises two factors: interaction promotion mechanism and platform technology. The social dimension contains three factors: social economy, social public events, and the clustering effect. Fuzzy DEMATEL analysis indicated that perceived utility, health information literacy, information clarity, and social economy are the key factors. ISM analysis revealed a 4-layer hierarchical structure of influencing factors from the superficial to the deep, with the social economy being the deepest-layer factor. Additionally, four key transmission paths were sorted out. Based on the research conclusions, four suggestions are proposed: Firstly, from the personal dimension, efforts should be made to mobilize the subjective role of users. Secondly, from the information dimension, the information quality and clarity for content creators and sharers should be improved. Thirdly, from the platform dimension, active cooperation with content sharers should be pursued and the interaction mechanism should be optimized. Finally, from the social dimension, the government should promote the development of the health popular science industry. In subsequent studies, empirical tests (such as structural equation modeling and fsQCA) can be incorporated to ensure the reliability and validity of the theory.

[Purpose/Significance] The governance of health medical data is fundamentally challenged by the "protection-sharing" paradox: the critical need to safeguard sensitive personal information often conflicts with the desire to utilize these data for public benefit. This issue is particularly pressing under China's "Healthy China" initiative, which promotes data sharing while the rapid expansion of medical APPs has led to increasing data misuse incidents. Existing research has extensively explored technological solutions such as blockchain, but a significant gap remains in understanding the dynamic, strategic interactions among the key stakeholders - government regulators, APP operators, and users - who operate with bounded rationality. This study addresses this gap by constructing a tripartite evolutionary game model. Its primary significance lies in dynamically modeling the co-evolution of strategies to identify critical leverage points, thereby providing a theoretical basis for designing effective collaborative governance mechanisms that can reconcile data protection with utilization and ensure the sustainable development of the health data ecosystem. [Method/Process] This study established a three-party evolutionary game model involving government regulators, medical-health APP operators, and users, based on the core assumption of bounded rationality. The model incorporated a comprehensive set of parameters, including direct benefits, various costs (compliance, regulatory), data risks, and network benefits under different regulatory scenarios. Replicator dynamic equations were derived for each party to mathematically describe the evolution of their strategy choices over time. The stability of the system's equilibrium points was rigorously analyzed using Lyapunov's first method to identify key stability thresholds. To validate the theoretical analysis and explore the dynamic evolutionary paths, numerical simulations were conducted using MATLAB. These simulations tested the impact and sensitivity of critical parameters - such as user-perceived data risk under operator self-discipline, user network benefits under dynamic regulation, government compliance rewards, and penalties for overdevelopment - from various initial strategy combinations. [Results/Conclusions] The analysis yielded several critical findings. First, users' authorization decisions are highly sensitive to the operational context, and they are significantly positively influenced by the perceived level of operator self-discipline and the observed intensity of government dynamic regulation. Enhancing user network benefits under effective regulation and reducing perceived data risks are paramount to encouraging authorization. Second, for APP operators, increasing government penalties for overdevelopment acts as a powerful deterrent, rapidly steering operators towards compliance. In contrast, government financial rewards for compliance, while effective, must be carefully balanced against their potential fiscal burden, which can slow the government's own stabilization into a dynamic regulatory role. Third, the system exhibits strong path dependence, capable of converging towards either an inefficient equilibrium (Non-Authorization, Overdevelopment, Passive Regulation) or the optimal Pareto state (Authorization, Self-discipline, Dynamic Regulation), depending heavily on initial conditions. The study concludes that resolving the paradox requires a multi-faceted strategy: advancing and ensuring robust anonymization technologies, implementing intelligent graded supervision that combines incentives and punishments, and firmly establishing institutional safeguards for user data sovereignty to build essential trust. A key limitation is the omission of data leakage risks from government data openness. Future work will integrate empirical data and consider user heterogeneity to refine the model.