[Purpose/Significance] The use of large language models (LLMs) for patent text mining has become a major research topic in recent years. However, existing studies mainly focus on the application of LLMs to specific tasks, and there is a lack of systematic evaluation of the application effects of fine-tuned LLMs across multiple scenarios. To address this problem, this study takes ChatGLM, an open-source LLM that supports local fine-tuning, as an example. We conduct a comparative evaluation of three types of patent text mining tasks-technical term extraction, patent text generation, and automatic patent classification-under a unified experimental framework. The performance of fine-tuned models is compared from six aspects: different training data sizes, different numbers of training epochs, different prompts, different prefix lengths, different datasets, and single-task versus multi-task fine-tuning. [Method/Process] This study was based on an open-source LLM and carried out fine-tuning research for specific patent tasks in order to clarify the impact of different fine-tuning strategies on the performance of LLMs in patent tasks. Considering task adaptability, model size, inference efficiency, and resource consumption, ChatGLM-6B-int4 was selected as the base model, and P-Tuning V2 was adopted as the fine-tuning method. Three categories of patent tasks are included: extraction, generation, and classification. The extraction task is patent keyword extraction. The generation tasks include: 1) innovation point generation; 2) abstract generation based on a given title; 3) rewriting an existing title; 4) rewriting an existing abstract; 5) generating novelty points based on an existing abstract; 6) generating patent advantages based on an existing abstract; and 7) generating patent application scenarios based on an existing abstract. Six experimental comparison dimensions are designed: 1) different training data sizes; 2) different numbers of training epochs; 3) different datasets with the same data size; 4) different prompts under the same task and data; 5) different P-Tuning V2 prefix lengths with the same training data; and 6) single-task fine-tuning versus multi-task fine-tuning. Two type of evaluation metrics were used. For extraction and generation tasks, the BLEU metric based on n-gram string matching was adopted. For classification tasks, accuracy, recall, and F1 score were used. [Results/Conclusions] Based on the fine-tuning results, several conclusions were obtained. First, a larger training data size does not always lead to better performance. Second, the appropriate number of training epochs depends on the data size. Third, under the same data distribution, different data subsets have limited influence on performance. Fourth, under the same task and dataset, different prompts have little impact on model performance. Fifth, the optimal prefix length is closely related to the training data size. Sixth, for a specific task, single-task fine-tuning performs better than multi-task fine-tuning. These conclusions provide reference and guidance for fine-tuning LLMs in practical patent information work.

[Purpose/Significance] Against the backdrop of national innovation-driven development strategies and the pressing need to enhance the efficiency with which scientific and technological achievements are transformed within universities, university libraries are undergoing a critical transition. They are shifting from being traditional, passive information providers to becoming proactive, embedded partners in the research and innovation value chain. However, this transition is often hampered by inherent limitations in traditional service models. This study, therefore, posits artificial intelligence (AI) as a pivotal enabler and investigates the specific mechanisms through which AI technologies can empower university libraries to achieve deep, systemic integration into the entire lifecycle of technology transfer. The research aims to provide a comprehensive theoretical framework for understanding this transformation and offer actionable, evidence-based practical pathways for academic libraries to redefine their functional boundaries and substantially strengthen the institutional support ecosystem for university technology transfer. [Method/Process] This research employs a qualitative multi-case study design, underpinned by an analytical framework constructed around the four critical, sequential stages of the technology transfer lifecycle: 1) research topic selection and project initiation, 2) research and development, 3) project conclusion and evaluation, and 4) marketization and industrialization of outcomes. Case selection followed purposive sampling criteria to ensure representation across diverse contexts, including domestic and international universities, as well as varied library types. The primary data comprised detailed case descriptions from published academic literature, institutional reports, and official service platforms. Within this staged framework, the analysis focuses on two intertwined dimensions at each phase: the evolution of the library's core service functions and the transformative impact of AI empowerment. Through a comparative cross-case analysis, this study examines how specific AI technologies augment traditional services, fundamentally changing the role and value proposition of libraries. [Results/Conclusions] The results show that through intelligent information analysis, knowledge association, data mining, and precise matching, AI can promote university libraries to shift from resource supply-oriented support to collaborative services that run through the entire lifecycle of technology transfer. This transformation manifests across the four-stage lifecycle as a shift: from providing literature to forecasting opportunities at the initiation phase; from offering patent data to navigating R&D pathways and risks during development; from archiving outputs to assessing value and potential at conclusion; and from disseminating information to intelligently brokering industry partnerships at the commercialization phase. Synthesizing these stage-specific transformations, this study constructs a novel, integrated service framework. This framework explicitly links specific AI capabilities with the redefined core functions of the library at each stage, illustrating the transition from a linear support model to a dynamic, AI-augmented ecosystem wherein the library serves as a central intelligence node. Meanwhile, this study reveals practical challenges in current practices, including ambiguous organizational boundaries, insufficient professional capabilities, and imperfect evaluation mechanisms oriented toward technology transfer. Correspondingly, it proposes strategies such as clarifying collaborative positioning, strengthening the construction of AI-empowered service capabilities, and improving technology transfer-oriented evaluation mechanisms to promote the sustainable development of AI-empowered research services in university libraries.

[Purpose/Significance] This study investigates the operational practices and strategic development pathways of intelligent consultation services in libraries globally, specifically under the impetus of artificial intelligence (AI) large language models (LLMs). By conducting a systematic analysis of representative case studies, we examine the applied technologies, emerging service models, and measurable efficacy of these AI-enhanced services. The research holds significance in offering actionable insights for the effective implementation of AI within the library sector. It aims to guide the evolution of intelligent consultation toward greater innovation and cultural-contextual adaptability, thereby providing both theoretical underpinning and practical guidance for the localized development of smart library ecosystems. [Method/Process] Employing a comparative case study methodology, this research selected 30 representative libraries from diverse international and domestic contexts as its subjects. Data were primarily gathered through structured online surveys and content analysis of publicly available service interfaces, systematically capturing the scope, functionality, and operational status of their intelligent consultation services. The analysis focused on characterizing technological applications-identifying core LLM integrations, typical functionalities, and architectural highlights. It further integrated findings to compare and contrast prevailing service models and implementation variances. Subsequently, the study conducted a multidimensional comparative assessment of the practical service effectiveness enabled by AI large models, evaluating performance across four key areas: service response efficiency and accuracy; capabilities in resource organization and structured knowledge management; tangible improvements in user service experience; and degree of service model innovation. [Results/Conclusions] The findings indicate that AI large model-driven intelligent consulting services exhibit pronounced advantages in key operational metrics, including enhanced response efficiency, superior knowledge synthesis and management capabilities, enriched user interaction experiences, and the facilitation of novel service paradigms. However, a comparative analysis reveals significant disparities among libraries concerning the depth of technological integration, the sophistication of service offerings, and the level of cultural and linguistic adaptation achieved. In response, the study proposes targeted strategic recommendations from three interrelated perspectives: nuanced technological application, user-centered service design, and collaborative ecosystem construction. It advocates for libraries to prioritize the synergistic balance between technological capability and humanistic service values, to achieve deeper integration with localized and institutional knowledge repositories, and to institute mechanisms for continuous service evaluation and iterative optimization. These approaches are essential for fostering more efficient, inclusive, and sustainable development of intelligent consultation services. Future research directions should encompass longitudinal studies on service effectiveness, the integration of multimodal interactive capabilities, and the formulation of ethical guidelines and governance frameworks for AI deployment in library services.

[Purpose/Significance] With the rapid integration of generative artificial intelligence into library services, user trust in information has begun to exhibit a new pattern characterized by high usage, low certainty, and increased reliance on institutional guarantees. Existing studies on online credibility, artificial intelligence generated content (AIGC) applications and library innovation have mostly examined either technical performance, information literacy, or governance issues in isolation. Few have systematically analyzed how specific AIGC features, user capabilities and the institutional environment of libraries jointly shape multi dimensional user trust. This study focuses on AIGC supported services in public and academic libraries and constructs a comprehensive analytical framework linking technological signals, user ability and library based institutional mediation to the formation of cognitive, emotional and behavioral trust. The paper contributes to the refinement of trust theory in digital information environments by providing empirical evidence from a large-scale sample in China. It also offers actionable insights for libraries seeking to deploy AIGC while maintaining or enhancing their role as trusted public knowledge institutions. [Method/Process] The study is grounded in classic research on cognitive authority and online credibility, and combined with recent work on AIGC, knowledge services, information literacy and library governance. It conceptualizes user trust as a three dimensional construct comprising cognitive, emotional and behavioral components. AIGC related technological features are operationalized along three axes: perceived content quality, generation transparency and interactivity. User capability is measured through standardized digital literacy tests and indicators of professional background, while the library environment is captured by the presence of institutional arrangements such as usage guidelines, staff verification, result labelling and risk reminders. Data were collected through a large-scale questionnaire survey in ten public and academic libraries in Henan Province, yielding 2 347 valid responses. After data cleaning and reliability and validity checks, the study employed a combination of structural equation modelling, two stage least squares estimation, threshold regression, spatial autoregressive models, dynamic panel system GMM estimation, quantile regression and finite mixture models. This sequential strategy allowed for simultaneous identification of structural paths, endogenous relationships, non linear and moderating effects, spatial spillovers and temporal dependence, as well as heterogeneous trust formation patterns across user groups. [Results/Conclusions] The findings confirm that user trust in AIGC enabled library services is best understood as a three dimensional structure, in which cognitive trust influences emotional trust, and both jointly shape behavioral trust. Content quality and generation transparency exert strong and robust positive effects on cognitive trust, while interactivity mainly enhances emotional trust and indirectly affects behavioral intentions. Digital literacy and professional background introduce clear threshold and amplification effects: when user capability is below certain levels, improvements in content quality and transparency have limited impact on trust, but above these thresholds the marginal effects increase markedly. Library level institutional arrangements, including human review, explicit labelling and standardized usage rules, not only raise overall trust levels, but also significantly strengthen the effects of technological signals, sometimes to a degree comparable with individual level capability factors. Spatial and dynamic analyses show that trust exhibits both spillover and path dependence: practices in one library can influence neighbouring institutions through user mobility and word of mouth, and positive or negative experiences accumulate into longer term evaluations. The study suggests that libraries should treat trust building as a core design objective when introducing AIGC, embed transparency and quality signals into interfaces and metadata, establish robust verification and correction workflows, and provide differentiated services for users with different literacy levels and professional backgrounds. The limitations include the concentration of data in one province and the use of primarily macro-level instruments for identifying causation. Future research could extend the framework to cross regional and cross type libraries, compare specific functional scenarios such as reference services and reading promotion, and further integrate trust analysis with broader issues of library governance, literacy education and responsibility allocation in AIGC ecosystems.

[Purpose/Significance] The effective flow of agricultural knowledge from innovation sources to fields is a core component of agricultural modernization. However, a persistent "structural knowledge gap" exists between macro-level knowledge supply and the micro-level needs of farmers, which traditional top-down extension systems often fail to bridge due to issues such as information decay, a lack of feedback, and poor contextual adaptation. In the context of promoting the high-quality development of rural public cultural services, grassroots reading spaces (e.g., rural libraries and village reading rooms) face a critical imperative to evolve beyond their traditional role as static repositories of books. This study reimagines grassroots reading spaces as dynamic "knowledge nodes" within rural socio-information ecosystems. The primary significance of this research lies in its innovative integration of public governance and knowledge management theories to construct a novel "node-interface-flow" analytical framework. It moves the discourse forward from predominant concerns with resource allocation or technology access to a deeper investigation of how internal governance mechanisms fundamentally shape these spaces' capacity to process and diffuse knowledge. By doing so, it positions the study at the intersection of rural studies, public administration, and knowledge science, offering a refined theoretical lens to understand and design rural knowledge infrastructure. Its practical importance stems from providing evidence-based, mechanistic explanations and actionable pathways for transforming these ubiquitous facilities from venues of "cultural provision" into active agents of "knowledge empowerment" for rural communities. [Method/Process] To uncover the mechanisms through which collaborative governance influences knowledge flow, this study employed a sequential explanatory mixed-methods design (QUAN → QUAL). The research was empirically grounded in a comparative case study of three rural reading spaces in China, deliberately selected through theoretical sampling to represent three distinct ideal-typical governance models: Jiangyin (exemplifying a deep contractual model involving long-term institutional agreements between local government and a vocational college), Liancheng (representing an administrative-dominant model operating within a standardized county-branch library system), and Yuhang (illustrating a social collaborative model based on government-purchased services from local social organizations). The methodological appropriateness of this multi-case comparative approach lies in its capacity to maximize variation in the key independent variable (governance model) while controlling for contextual factors, thereby allowing for clearer causal inference regarding the model's impact. Data were collected from March to August of 2024. The quantitative phase involved a structured questionnaire survey administered to 438 farmers across the villages served by the three case spaces (from 480 distributed, 91.3% valid response rate). The survey instrument was designed to measure key variables derived from the theoretical framework, including perceived interface quality (e.g., resource relevance, expert accessibility), knowledge acquisition, community knowledge sharing, and technology adoption intention. Reliability and validity tests (e.g., Cronbach's α, K-R20) confirmed the robustness of the measures. The subsequent qualitative phase comprised 38 in-depth, semi-structured interviews with space managers, active farmers, and key partners, supplemented by participatory observation and archival analysis. This phase aimed to provide rich, contextual insights into the operational mechanisms linking governance rules, interface functioning, and knowledge flow patterns. Quantitative data were analyzed using SPSS for ANOVA and regression analysis to test performance differences and mediation effects, while qualitative data were thematically coded using NVivo to elucidate underlying processes. [Results/Conclusions] The findings confirm the proposed "governance model → interface characteristics → flow efficacy" mechanism. The deep contractual model, through its "embedded interface," successfully couples strong formal institutional guarantees (e.g., mandated expert deployment, resource co-selection) with derived informal trust relationships from long-term embeddedness. This combination significantly drives the deep, closed-loop flow of highly complex, codified knowledge, completing cycles from external input to local application and feedback. In contrast, the social collaborative model's "networked interface," characterized by vibrant informal community networks activated by skilled social organizers, proves far more effective in stimulating the horizontal sharing, exchange, and co-creation of tacit knowledge within the community. The administrative-dominant model, with its standardized formal interface and underdeveloped informal connections, demonstrates limited efficacy, often resulting in interrupted, one-way knowledge flow. Based on these insights, the study proposes a two-dimensional model of "institutional depth" versus "networked breadth" to describe the unique effectiveness of different governance models. Based on these empirical results, three concrete policy and management recommendations have been proposed to foster responsive rural knowledge nodes: 1) shifting performance evaluation and resource allocation from static input metrics towards a focus on dynamic "interface capability"; 2) designing and institutionalizing specialized "knowledge broker" programs to staff these interfaces with trusted, skilled intermediaries; and 3) initiating collaborative "local knowledge repository" projects to systematically capture, digitize, and valorize indigenous community wisdom. The study acknowledges limitations regarding the generalizability of findings from a three-case comparison and suggests future research directions, including longitudinal studies to observe interface evolution, social network analysis to precisely map relational structures, and exploration of how digital "smart interfaces" might integrate with the social interfaces examined here to create new paradigms for rural knowledge service.

[Purpose/Significance] As the user base grows, the number of online comments is increasing rapidly. The massive volume of comments has broadened the innovative thinking of enterprises and provided more diverse innovative options, but it has also brought about the problem of information overload. Therefore, in the face of the massive amount of online user comments, how to use efficient and precise methods to mine information with practical value, effectively integrate valuable information and identify product innovation opportunities, and transform it into high-quality resources for enterprise product innovation has become a hot topic of great concern in both academic and industrial circles. Against this backdrop, studying how to identify product innovation opportunities based on online reviews is of great theoretical significance and practical value. Unlike previous studies, this paper uses the BERT model to accurately filter out negative user comments and identify key demand points. This article also combines the characteristics of ordinary users and leading users, integrates dual-source data of user comments from e-commerce platforms and online communities, and associates the demand issues of ordinary users with the suggestions of leading users, which can more accurately identify product innovation opportunities. [Method/Process] First, we collected and pre-processed ordinary user comment data and leading user comment data. Second, the BERT model and LDA topic model were used to categorize the sentiment and cluster the comment data to mine the problems of ordinary users and suggestions of leading users. Finally, based on semantic similarity analysis, problem-suggestion topic mapping was realized to identify product innovation opportunities with high innovation value. [Results/Conclusions] This paper constructed a problem-suggestion product innovation opportunity identification method driven by dual-source data, and selected the action camera as a case to elaborate in detail on the specific practice of the proposed method in the field of product innovation. Through case analysis, the feasibility of the proposed method of product innovation was verified, providing an operational reference basis for enterprises on how to efficiently recommend product innovation work. However, this paper still has certain limitations and needs to be improved with more abundant data in subsequent studies. First, the data collected in this article mainly come from e-commerce platforms and online community platforms. Although this data contain a large amount of user information, there are still deficiencies. In the future, we will introduce more data sources, such as news media and technology websites to obtain more comprehensive and diverse data. Second, this paper has only conducted case application research in the field of intelligent digital products. In the future, we need to further explore more fields, such as smart wearables and whole-house intelligence, to enhance the universality of the product innovation opportunity identification framework constructed in this paper.

[Purpose/Significance] Seoul Outdoor Library has not only gained recognition from Seoul citizens, but has also received awards from the International Federation of Library Associations and Institutions (IFLA) for two consecutive years. Since its opening, it has served 8 million users, with a user satisfaction rate of 96.6%. Moreover, it attracts the attention of the library industry both domestically and internationally. Based on this, this paper extracts replicable and scalable practical experiences and insights from the successful case of Seoul outdoor library. Its research significance lies in both addressing the dilemma of "practice taking precedence over theory" in outdoor libraries, filling the academic research gap in this field, and providing practical guidance for the long-term, high-quality development of outdoor libraries in China. [Method/Process] The research conclusions drawn from single case study methods often possess greater enlightenment and relevance to reality. Based on this, the paper analyzes the basic situation of Seoul Outdoor Library through a single case study method. Moreover, the paper adopts the "triangulation verification" multi-source data collection method to enhance the validity and reliability of the research. We found that the main service contents include book reading services, space services, art literacy education, tourism information services, and policy display and promotion services. In addition, Seoul Outdoor Library exhibits green integration and sustainability in its design, flexibility and decentralization in spatial characteristics, openness and flexibility in scene characteristics, and emphasizes interaction and human-centered service. The innovative value of Seoul Outdoor Library is reflected in the coexistence of low-cost space supply and high satisfaction, deepening the connection between libraries and public affairs, and the organic integration of social and economic benefits. [Results/Conclusions] The paper holds that the development of outdoor libraries in China should start with several aspects. Firstly, outdoor libraries should be based on observation to promote the "rediscovery of libraries" initiative. For example, outdoor libraries rediscover the new value of space, the new role of librarians, and the new connotation of resources. Secondly, outdoor libraries should be endowed with values and infused with soul, making full use of local resources to endow them with spiritual cores. Thirdly, outdoor libraries should shape their output, and optimize scene construction. Finally, outdoor libraries should nourish the heart through implementation, deeply cultivate emotional experiences, and allow users to feel a sense of belonging through humanistic details. Of course, the paper inevitably has limitations. Future research will expand case samples to gain a more comprehensive understanding of outdoor libraries and facilitate their high-quality development in China.

[Purpose/Significance] With the continuous advancement of national governance modernization and the rapid development of artificial intelligence (AI) technologies, emotional-functional embodied intelligence has become integral to grassroots social governance. This development not only reshapes traditional governance tools but also triggers profound reflections on the balance between instrumental rationality and value rationality. In this context, systematically examining the internal mechanisms and potential risks associated with the integration of emotional-functional embodied intelligence into social governance can provide both theoretical enrichment and practical guidance for technology-enabled governance modernization. [Method/Process] Based on Max Weber's "tool-value" dichotomy, this study focuses on key issues concerning the influence mechanisms, risk boundaries, and regulatory pathways of emotional-functional embodied intelligence in social governance. By situating the analysis within concrete scenarios of its embedding in social governance practices, the research combines theoretical reflection with contextual examination to explore how emotional-functional embodied intelligence reshapes governance structures and processes. [Results/Conclusions] The findings reveal that AI, embodied intelligence, and emotional-functional embodied intelligence differ significantly in terms of technological architecture, functional form, and modes of integration into social governance. While AI optimizes decision-making through data empowerment and embodied intelligence delivers services through physical interaction, emotional-functional embodied intelligence achieves full-process and in-depth integration into social governance by relying on affective linkage. It forms an integrated structural system composed of the demand, intelligence, action, and support layers, thereby enabling coordinated governance operations that combine rational decision-making with emotional interaction. Through three core mechanisms - intelligence embedding, human-machine coupling and feedback-driven iteration, emotional-functional embodied intelligence is able to simultaneously accomplish rational decision-making tasks and emotional interaction objectives. However, the embedding of emotional-functional embodied intelligence in social governance also implies dual structure of risks. On one hand, it may amplify traditional risks inherent in AI technologies, such as algorithmic dependence and blurred responsibility attribution. On the other hand, it may generate new forms of context-specific risks, including emotional-cognitive alienation, value-guidance deviation, and the reconstruction of governance authority. To address these challenges, it is necessary to construct a full-chain regulatory framework for accountability and establish full-process technological safeguards encompassing ex-ante prevention, in-process monitoring, and ex-post traceability. Concurrently,it's essential to articulate value-oriented principles for emotion-informed governance and clarify a human-machine collaborative governance framework in which human actors retain primary authority while intelligent technologies play an auxiliary role. Through these coordinated measures, effective risk regulation and rational balance can be achieved in the application of emotional-functional embodied intelligence in social governance.

[Purpose/Significance] As data become a strategic resource in the digital economy, its quality directly affects the efficiency of value creation and the effectiveness of public governance. However, with the continuous expansion of data scale and the deepening of application scenarios, pervasive quality issues - such as inconsistencies, errors, and redundancies - have emerged as a significant bottleneck restricting the release of data element potential. High-quality public data are particularly critical for empowering government decision-making and optimizing public services. Addressing the urgent practical need for high-quality data supply, this paper relies on the public basic databases (specifically the Population Database and Legal Entity Database) of a representative city to construct a scientific, systematic, and operable data quality assessment system. The study aims to diagnose existing quality defects in these foundational assets and provide theoretical support and actionable references for relevant departments to transition from passive data management to active quality governance. [Method/Process] To ensure the assessment is both scientifically rigorous and practically applicable, this study establishes a comprehensive evaluation framework based on domestic and international research, combined with the national standard GB/T 36344-2018 and local data characteristics. The framework comprises a hierarchical structure with 6 primary indicators (Normativity, Integrity, Consistency, Accuracy, Timeliness, and Accessibility), 17 secondary indicators, and 61 specific detection items. The study employs a dual-track assessment methodology integrating automated detection tools with manual verification. Automated SQL scripts and rule engines are utilized for the large-scale quantitative detection of intrinsic dimensions, while manual checks and interviews address contextual dimensions. This methodology was applied to conduct a multi-dimensional evaluation of 1 367 data items across 102 datasets in the city, ensuring a thorough analysis of the data status. [Results/Conclusions] The evaluation results indicate that while the overall construction of the city's public basic databases is positive, multidimensional quality issues persist. Specifically, the assessment revealed problems such as data coding errors, non-standardized classification, missing data items, missing or duplicate primary keys, inconsistent formats, the presence of illegal characters or outliers, and data delays or discontinuations. To address these challenges, the paper proposes four systematic improvement strategies: 1) To unify data standards and coding systems to ensure consistency across departments; 2) To construct a full-process quality control mechanism covering data collection, storage, and usage; 3) To strengthen technical platform support by implementing real-time monitoring and intelligent warning capabilities; and 4) To improve organizational synergy and institutional guarantees to solidify the management foundation. These measures are intended to optimize data supply quality and support the support the high-quality and sustainable development of the data element market.

[Purpose/Significance] The accelerated digital transformation of public cultural services has fundamentally reshaped modes of service delivery, governance frameworks, and citizen engagement. Exploring how digital technologies empower the high-quality development of public cultural services is essential for designing a modern, equitable, and efficient service system. This study contributes to the existing literature by systematically investigating not only the direct effects of digital technologies but also threshold, regional heterogeneity, spatial spillovers, and mediating mechanisms. This clarifies how digital innovation interacts with governance capacity and institutional environments. Unlike previous research, which relied mainly on descriptive or single-method analyses, this study employs an integrated empirical framework. This framework captures the dynamic and multidimensional nature of digital empowerment within the context of public service. It enriches the theoretical and practical understanding of digital governance. [Method/Process] This study employs panel data from 31 Chinese provinces over the period 2015-2023 to systematically investigate how digital technologies influence the high-quality development of public cultural services. A combination of fixed-effects models, mediating-effects models, threshold regression models, and spatial econometric models was used to capture direct, nonlinear, regional, spatial, and mediating effects. To control for potential confounding factors, fiscal expenditure, population density, and cultural literacy were incorporated as covariates. The analysis drew on theoretical foundations conceptualizing digital technology as a new productive force and was supported by empirical data from national statistical yearbooks, digital finance indices, and governance performance indicators, ensuring both methodological rigor and contextual relevance. [Results/Conclusions] Digital technology significantly promotes the high-quality development of public cultural services, with measurable positive effects for each incremental increase in the digital technology development index. The influence exhibits a nonlinear threshold pattern, reflecting a "promotion-weakening-enhancement" trajectory, highlighting the necessity of integrating technological applications with governance structures, resource allocation, service design, and public digital literacy. Regional analyses reveal stronger effects in the central and western provinces, suggesting that digital technologies can help mitigate service disparities under supportive policy frameworks. The spatial econometric results indicate positive spillover effects on neighboring regions, while the mediation analysis identifies government governance capacity as a key mechanism through which technological inputs translate into service outcomes. Policy implications include reinforcing digital infrastructure, enhancing institutional support, implementing region-specific strategies, fostering inter-provincial coordination, and strengthening government-led service integration. The study has limitations, including the possibility of potential unobserved concurrent causal pathways, Future research should adopt configurational methods such as qualitative comparative analysis in future research to further elucidate the complex, multicausal dynamics of digital technology empowerment in public cultural services.

[Purpose/Significance] Under the background of the digital economy, problems such as the difficulty in integrating multi-source heterogeneous data, low efficiency in matching supply and demand, and imbalance between security and openness in library data opening and sharing have restricted traditional technologies and service models from breaking through the bottlenecks. Large language models (LLMs) offer a new path to break through this predicament. This study aims to improve the theoretical system of technology that empowers the open sharing of library data. It also aims to fill the gap in existing research, which mostly focuses on general technologies and lacks systematic adaptation to library scenarios. Additionally, this study aims to provide theoretical and practical support for libraries to transform from data custodians to knowledge enablers, which will support the high-quality development of the industry. [Method/Process] Based on the elaboration of the practical impact of LLMs on the open sharing of library data, this paper analyzed the connotation, essence and characteristics of library data open sharing empowered by LLMs Based on this, the internal logic of LLMs driving the open sharing of library data was discussed, and the implementation path was explored. [Results/Conclusions] The open sharing of library data based on LLMs is manifested as a hierarchical leap in the value of data elements from basic integration, demand matching to decision support. This process needs to be efficiently advanced through human-machine collaboration on the supply side, user participation on the demand side, and cross-domain linkage on the ecosystem side. It should run through the entire life cycle of data production, governance, circulation, and application. Based on this, four guarantee strategies were proposed. In terms of technical architecture, we should adopt the "general model + domain fine-tuning" mode to adapt to the characteristics of library data. Efforts should be devoted to establishing a full-process quality control and hierarchical desensitization mechanism in data governance. In terms of talent cultivation, we should build a "business + discipline + technology" compound team. In terms of ethical construction, a full-process review and user rights protection system should be established. In the future, it is possible to further explore the in-depth adaptation of LLMs with the special collection resources of libraries, as well as the construction of a dynamic and elastic security governance framework, to promote the ecological development of industry data openness and sharing.

[Purpose/Significance] Generative Artificial Intelligence (GAI) has rapidly reshaped the landscape of social information dissemination, bringing unprecedented network public opinion risks-such as large-scale disinformation spread, algorithmic bias-induced social inequality, extreme emotional polarization, and model hallucinations leading to cognitive deviations-that significantly amplify the complexity, suddenness, and cross-domain spillover effects of public opinion evolution. These risks not only undermine the authenticity and order of information ecosystems but also pose severe challenges to social governance, public trust, and policy-making efficiency, making accurate identification, quantitative assessment, and early warning an urgent academic and practical task. Existing research has obvious limitations: single-dimensional assessment frameworks fail to capture GAI's multi-faceted and interrelated risks, such as the concealment of generated content, algorithmic recommendation amplification and cross-platform diffusion; traditional models such as basic BP neural networks suffer from susceptibility to local optima and poor generalization, inadequately adapting to the non-linear, dynamic, and high-dimensional attributes of GAI-generated content. To address these gaps, this study constructed a 4-dimensional risk assessment index system (content, dissemination, sentiment, and user) and proposed a GA-optimized BP neural network model, which will enrich public opinion management theories in the AI era and provide practical, efficient tools for precise risk control. It will contribute to the construction of a safe, orderly, and trustworthy online space. [Method/Process] A mixed research method with solid theoretical foundations (information communication theory and intelligent optimization algorithms) and empirical support was adopted: Ten typical GAI-induced public opinion events were selected from Sina Weibo (selection criteria: views ≥1 million, original posts ≥60, covering technology, society, public affairs, and consumption fields). Following a four-stage evolutionary model (formation, outbreak, mitigation, and recovery) and four early warning levels (Level I-IV, corresponding to binary outputs 1000, 0100, 0010, 0001) as specified in national emergency management standards, samples were systematically categorized into four evolutionary stages and corresponding risk grades. A 12-indicator system covering content (authenticity, misleadingness, and professionalism), dissemination (speed, scope, and diffusion path), sentiment (intensity, polarization degree, and negative ratio), and user (influencing impact, participant activity, and interaction stickiness) dimensions was constructed. The weights of each indicator were determined to ensure objectivity, and data preprocessing was performed via min-max normalization to eliminate dimensional differences. A 4-layer BP neural network (12 input neurons, 2 hidden layers with 15 and 10 neurons respectively, and 4 output neurons) was built, with initial weights, thresholds, and hyperparameters (learning rate and iteration times) optimized by genetic algorithm (GA). A traditional BP model served as the control group, with 70% of data as the training set and 30% as the test set, and model performance was evaluated based on prediction accuracy. [Results/Conclusions] Experimental results confirm the significant superiority of the GA-BP model: its prediction accuracy reached 91.67%, 8.34 percentage points higher than the traditional BP model (83.33%). This verifies that GA optimization effectively improved model performance, enabling better capture of complex non-linear relationships among GAI-induced risk factors. The multi-dimensional index system successfully extracted core risk characteristics, realizing comprehensive identification and traceability of GAI-related public opinion risks. Limitations of this study include sample concentration on Chinese social platforms, limited case quantity, and narrow time span. Future research will expand cross-border, multi-language samples (e.g., Twitter, Facebook), enrich technical indicators (e.g., GAI content identifiability, algorithmic intervention intensity), and explore integration with deep learning models (e.g., LSTM, Transformer) to further enhance the generalizability, real-time performance, and intelligent decision-making support capabilities of the risk assessment system.

[Purpose/Significance] Under the strategic background of national cultural digitization and the high-quality development of public services, artificial intelligence generated content (AIGC) has become a core engine driving the digital and intelligent transformation of galleries, libraries, archives, and museums (GLAM). While AIGC offers unprecedented opportunities for content production and knowledge dissemination, current implementations often suffer from fragmentation, leading to new "data islands" and service barriers. Unlike previous studies, which treat GLAM institutions as a homogeneous whole, this paper aims to clarify the differentiated application paths of AIGC by distinguishing the unique "resource-technology-service" logic of each institution type. It seeks to reveal the structural causes of current collaborative dilemmas and construct a systematic collaborative development mechanism. This research is significant for breaking down institutional barriers, promoting the deep integration of cultural resources, and guiding GLAM institutions to shift from isolated technological upgrades to a clustered, symbiotic development model. [Method/Process] Adopting a digital ecosystem perspective, this study constructs a "Resource Attributes - Technology Adaptation - Service Goals" framework to systematically analyze the heterogeneous characteristics of the four institution types. The analysis reveals how distinct data morphologies - ranging from structured texts in libraries and semi-structured records in archives to multimodal artifacts in museums and unstructured works in art galleries - fundamentally dictate the differentiated deployment of generative text or vision models. By examining core capabilities including intelligent content twinning, editing, and creation, the study demonstrates how service goals strictly regulate technical choices: the emphasis on "access" and "trust" in libraries and archives necessitates technologies that ensure semantic accuracy and historical authenticity, whereas the pursuit of "experience" and "creativity" in museums and art galleries favors generative tools for immersive interaction and open-ended aesthetic expression. [Results/Conclusions] To address the identified challenges of fragmented development, the study proposes a tripartite collaborative development architecture consisting of a "Front-end Resource Layer," a "Mid-platform Technology Layer," and an "End-user Service Layer." The Front-end Resource Layer focuses on constructing a unified multimodal data foundation and standardized semantic ontology to bridge the semantic gap between heterogeneous institutional data. The Mid-platform Technology Layer advocates for the co-construction of an industry-specific general large model and a knowledge reasoning engine; by sharing API interfaces and computing power, this layer solves the high technical threshold and cost issues for smaller institutions, acting as a ubiquitous "industry capability hub." The End-user Service Layer aims to build a one-stop knowledge exploration portal and cross-domain expert workbenches, eliminating service isolation and creating integrated cultural scenarios. The study concludes that GLAM institutions must transition from "cultural containers" to "knowledge engines" through this architecture. Future research should further focus on copyright ethics, algorithmic governance, and new modes of human-machine collaboration to ensure the sustainable and trustworthy development of the digital cultural community.

[Purpose/Significance] Against the backdrop of intensifying global technological competition and the drive for scientific and technological progress under national innovation strategies, generative artificial intelligence (AI) technology, as an emerging disruptive technology, has had a profound impact on the economy and society through its widespread application. However, the diffusion of this technology in the market still faces numerous challenges. This paper aims to delve into the micro-level decision-making factors influencing enterprises' research and development (R&D) of generative AI technology, as well as the specific impact of user group interactions on the effectiveness of technology diffusion, by constructing a complex network evolutionary game model. The research seeks to uncover the inherent laws governing technology diffusion, providing a scientific basis for policymakers and corporate practitioners to promote the healthy development and effective diffusion of generative AI technology, thereby fostering comprehensive socio-economic progress. [Method/Process] This paper adopts the complex network evolutionary game model as the primary research method, integrating complex network theory, technological innovation diffusion theory, and social influence theory to construct a game model for corporate decision-making regarding generative AI technology. By incorporating the structural characteristics of complex networks and the dynamic mechanisms of evolutionary games, the study simulates the R&D decision-making processes of enterprises under varying conditions of user adoption rates, government subsidy levels, differences in technology benefits and costs, and technology spillover effects. Simultaneously, numerical simulation analysis is employed to explore the specific impacts of changes in these factors on the diffusion effectiveness of generative AI technology decisions, thereby thoroughly revealing the micro-mechanisms underlying technology diffusion. [Results/Conclusions] The research results indicate that an increase in user adoption rates significantly and positively drives the diffusion of generative AI technology, with moderate user dependency behaviors further accelerating this process. Government subsidies play a particularly prominent role in promoting technology diffusion when user adoption rates and the initial proportion of enterprises choosing R&D strategies in the network are low. However, as these proportions rise, the marginal effect of subsidies gradually diminishes. The difference in benefits between enterprises that develop generative AI technology and those that do not has a marked impact on technology diffusion, whereas the impact of cost differences is relatively minor. Furthermore, the spillover effects of generative AI technology may induce free-rider behaviors among other enterprises, hindering technology diffusion. Additionally, when the maturity level of generative AI technology is low, it reduces user trust in the technology, thereby inhibiting its widespread dissemination. Based on these conclusions, this paper proposes policy recommendations such as encouraging user participation, flexibly adjusting subsidy policies, enhancing technology maturity, and establishing intellectual property laws and regulations to facilitate the effective diffusion of generative AI technology.

Purpose/Significance The formulation of evidence-based science and technology policy critically relies on the timely and accurate provision of relevant, high-quality evidence. However, current evidence recommendation practices often suffer from significant limitations in both accuracy and efficiency, hindering the scientific rigor and intelligent application of evidence within the policy-making process. These shortcomings hinder policymakers' ability to leverage the most pertinent research and data, potentially leading to suboptimal decisions. Addressing this critical gap, this research proposes a novel knowledge graph-based evidence recommendation method. The primary objective is to substantially enhance the scientific foundation and intelligent capabilities of evidence utilization during policy formulation. This method aims to empower policymakers by providing more reliable, contextually relevant, and efficiently retrieved data support. Ultimately this will foster more robust, transparent, and demonstrably effective science and technology policies grounded in comprehensive research insights. Method/Process To achieve these objectives, this study systematically constructs a domain-specific knowledge graph meticulously centered on the intricate citation relationships between policy documents and academic research papers. This graph serves as the foundational semantic network representing entities (policies, articles, topics, authors, institute etc.) and their multifaceted interconnections. Most importantly, we introduce and adapt the Knowledge Graph Attention Network (KGAT) algorithm n an innovative way. Leveraging KGAT's sophisticated graph attention mechanisms, our model effectively captures and learns complex, high-order semantic relationships between policy requirements (represented as queries or specific nodes) and potential evidence sources (research paper nodes). This deep relational understanding enables nuanced evidence relevance scoring and personalized recommendation. To rigorously validate the proposed method's practical efficacy and performance, we conducted an extensive empirical study within the specific domain of agricultural science and technology policy. Furthermore, to demonstrate real-world applicability and provide a tangible tool for policymakers, we designed and implemented a fully functional Evidence Intelligent Recommendation System (EIRS). This system seamlessly integrates the core KG-based recommendation engine and incorporates advanced intelligent analysis capabilities. Significantly, EIRS supports an end-to-end workflow initiated by natural language policy questions posed by users, enabling intuitive interaction and precise, demand-driven evidence retrieval and recommendation. [Results/ Conclusions Experimental results, conducted on real-world datasets within the agricultural science and technology policy domain, demonstrate the superior performance of the proposed KGAT-based recommendation method. It consistently outperforms several state-of-the-art baseline algorithms across multiple key evaluation metrics, including precision, recall, normalized discounted cumulative gain (NDCG), and mean reciprocal rank (MRR). This quantitatively confirms its significantly stronger recommendation capability. In addition to quantitative metrics, the model inherently offers enhanced explainability due to the transparent nature of the knowledge graph structure and the attention weights learned by KGAT, allowing for insights into why specific evidence is recommended, based on its semantic connections to the policy query. Concurrently, the implemented EIRS has proven to be highly effective in practice. It efficiently identifies and recommends evidence resources exhibiting a strong match with complex policy requirements expressed in natural language. The system's successful deployment underscores its potential to tangibly augment the scientific underpinning of science and technology policy development. By effectively bridging the gap between vast research knowledge and specific policy needs through intelligent, accurate, and explainable recommendations, this research provides a novel, practical pathway towards realizing truly intelligent and rigorously evidence-based policy formulation processes. The methodology and system prototype offer a valuable and adaptable framework for various policy domains beyond the presented case study.

[Purpose/Significance] Chinese classical texts are central to preserving and transmitting traditional culture; however, promoting them among children has long faced many obstacles: the linguistic barrier posed by classical Chinese, the cognitive distance caused by cultural discontinuity, and the limitations of static and monotonous promotional forms. These challenges have often resulted in low levels of engagement and comprehension among young readers. The recent emergence of Sora-type video generation models, characterized by their ability to produce coherent long-form narratives, integrate multimodal information, and simulate spatially consistent scenes, opens up new opportunities for bridging this gap. This study aims to investigate how such models can be effectively employed in the promotion of Chinese classics among children, to evaluate their potential benefits and inherent risks, and to develop practical strategies that align technological capabilities with educational and cultural objectives. [Method/Process] This research adopts a combined approach of literature review, case study, and comparative analysis. First, it reviews existing literature on the application of artificial intelligence in reading promotion, highlighting current achievements and limitations. Second, it uses representative Chinese classics, including Shan Hai Jing, Strange Tales from a Chinese Studio (Liaozhai Zhiyi), and The Book of Songs (Shijing), to examine how Sora-generated videos function in different promotional contexts. Third, it constructs an analytical framework based on three interrelated dimensions: scenes, content, and approaches. Within this framework, the study identifies opportunities, delineates challenges, and proposes targeted countermeasures. [Results/Conclusions] Sora-type video generation can substantially enhance the promotion of Chinese classics among children. At the scene level, it allows traditional spaces to be extended into immersive and hybrid environments, thereby broadening access beyond classrooms and libraries. At the content level, it transforms abstract imagery and complex narratives into visual forms, reducing cognitive barriers and accommodating differentiated learning needs. At the approach level, it facilitates text-image complementarity, cross-media integration, and personalized recommendations, thereby strengthening engagement and sustaining reading motivation. However, the study also cautions against significant risks. These include the mismatch between generated content and specific promotional settings, the danger of oversimplification or distortion of classical texts, and the over-reliance on audiovisual materials that might undermine children's ability to engage in deep textual reading. To address these risks, the article proposes a threefold strategy: differentiated scene design, content transformation with cultural fidelity, and complementary pathways that ensure children transition from video to text.

[Purpose/Significance] Digital hoarding has emerged as a significant behavioral phenomenon in the digital age, particularly prevalent among social media users who engage in the excessive acquisition and retention of digital content. This behavior is further amplified by algorithmic recommendation systems that continuously personalize content delivery. Although existing research has examined individual psychological factors or platform characteristics using static approaches, it lacks a dynamic perspective to understand the co-evolutionary relationship between platform strategies and user behaviors. This study addresses this research gap by introducing evolutionary game theory as an innovative analytical framework. Theoretically, the significance lies in modeling the dynamic interactions between platforms' algorithmic adjustments and users' hoarding behaviors. This provides new insights into the adaptive mechanisms within socio-technical systems. From a practical standpoint, this research offers valuable implications for promoting healthier digital environments and developing sustainable governance models for platforms that balance commercial objectives with user well-being. [Method/Process] This study employs evolutionary game theory to model the dynamic interactions between social media platforms and boundedly rational users. This method is well-suited for analyzing how strategies co-evolve over time towards stable states. Based on literature from user behavior and platform economics, a game-theoretic model was developed. Numerical simulations in MATLAB analyzed evolutionary paths across four platform types (Instant Messaging, Public, Short Video, and Vertical Community), with the model calibrated against empirical typologies to investigate how key factors influence long-term outcomes. [Results/Conclusions] The simulation results reveal that the evolutionary path of the platform-user interaction system is highly sensitive to key parameters, ultimately converging to different evolutionarily stable strategies (ESS) under varying conditions. A principal finding is that a unilateral increase in algorithmic recommendation intensity by platforms, while potentially boosting short-term engagement, does not guarantee long-term benefits and may instead drive users towards non-hoarding strategies due to increased cognitive burden. Crucially, the reasonable regulation of recommendation intensity is identified as the key to achieving sustainable, positive interactions. Moderate algorithmic recommendations can effectively alleviate information overload, reduce the negative impacts of hoarding, enhance user experience and satisfaction, and ultimately increase long-term platform benefits, creating a win-win scenario. The study provides significant managerial implications, suggesting that platform operators should incorporate user well-being metrics into algorithm evaluation frameworks, moving beyond purely engagement-driven models. Differentiated governance strategies are recommended for various platform types, such as implementing intelligent filtering on instant messaging apps and content quality incentives on vertical communities. However, this study has limitations, primarily its assumption of user homogeneity, which overlooks the impact of individual differences in preferences and digital literacy. Future research should introduce user heterogeneity, explore multi-platform competition scenarios, and validate the model with empirical data to enhance its practical predictive power and application value.

[Purpose/Significance] The rapid proliferation of generative artificial intelligence (AI), exemplified by models like DeepSeek-R1, has precipitated a paradigm shift across various sectors, positioning AI literacy as an indispensable competency for the future workforce. University students, as digital natives and pivotal agents of technological adoption and innovation, stand at the forefront of this transformation. Their proficiency in understanding, utilizing, and critically evaluating AI technologies directly influences their academic performance, research capabilities, and long-term career adaptability. Although existing literature has begun to explore the conceptual landscape of AI literacy, a significant gap remains. There is an absence of a robust, empirically validated competency framework specifically tailored to the unique learning contexts, developmental needs, and future roles of university students within China's higher education system. This study aims to address this critical gap by constructing and validating a comprehensive AI literacy competency framework for college students. Its primary significance lies in its ability to move beyond theoretical discourse and provide an evidence-based model that can guide the systematical development of targeted training programs. This enriches the theoretical underpinnings of AI literacy education and offers practical guidance for cultivating high-quality talent equipped for the intelligent era. [Method/Process] This research employed a mixed-methods approach, integrating qualitative and quantitative methods to provide both theoretical grounding and empirical robustness. The study commenced with a qualitative phase utilizing the grounded theory methodology. A systematic analysis of 112 core academic publications (2019-2024) from databases such as CNKI and Web of Science was conducted. Through a rigorous process of open coding, axial coding, and selective coding, facilitated by NVivo11 software, we extracted 300 initial concepts, which were subsequently synthesized into 26 sub-categories and ultimately 4 main categories. This process resulted in the preliminary construction of a four-dimensional AI literacy competency framework. Following this, a quantitative phase was implemented to test and refine the framework. A detailed questionnaire was developed based on the identified dimensions and indicators. Utilizing a five-point Likert scale, the questionnaire measured 26 variables corresponding to the framework's sub-components. A total of 586 valid responses were collected from undergraduate students across universities in Jiangsu Province, China. The dataset was randomly split into two halves. The first subset (N=293) underwent exploratory factor analysis (EFA) using SPSS to uncover the underlying factor structure and assess the internal consistency reliability via Cronbach's alpha. The second subset (N=293) was subjected to confirmatory factor analysis (CFA) using AMOS to verify the hypothesized factor structure, evaluate model fit indices (e.g., CMIN/DF, CFI, TLI, RMSEA), and establish convergent and discriminant validity by examining average variance extracted (AVE) and composite reliability (CR). [Results/Conclusions] The empirical analyses strongly support the validity and reliability of the proposed competency framework. The EFA clearly identified four distinct factors that aligned perfectly with the predefined dimensions, with a total variance explained of 69.916% and all factor loadings exceeding 0.6. The CFA results demonstrated excellent model fit (CMIN/DF=1.921, CFI=0.950, TLI=0.943, RMSEA=0.056), confirming the structural integrity of the framework. Furthermore, all constructs exhibited high internal consistency (Cronbach's α>0.90) and satisfactory convergent (AVE>0.5, CR>0.7) and discriminant validity. The finalized framework, therefore, comprises four interconnected core dimensions: AI Cognition (encompassing knowledge of basic concepts, applications, value, and risks), AI Skills (covering practical abilities from tool usage and programming to critical evaluation and innovation), AI Ethics (emphasizing social responsibility, privacy, intellectual property, and legal compliance), and AI Thinking (fostering higher-order cognitive abilities like computational, critical, and systemic thinking). Based on this validated framework, the study proposes a systematic and multi-faceted training system. This system outlines clear training objectives, identifies key stakeholders (e.g., university libraries, teaching centers, schools, and external enterprises), designs layered training content and pathways corresponding to each dimension, and suggests implementation strategies focusing on faculty development, a comprehensive assessment and feedback mechanism, and the strategic integration of AI-related resources. The main limitation of this study is that the respondents of the questionnaire were primarily college students during the empirical test stage. Future research can include teachers, business employers, and AI experts to modify and improve the index weight and content of the competency framework from multiple perspectives. This can be done through the Delphi method, expert interviews, and other methods, so as to enhance the framework's authority and universality.

[Purpose/Significance] The rapid expansion of artificial intelligence generated content (AIGC) is transforming how intellectual property (IP) literacy is cultivated in universities. Conventional approaches, often constrained by disciplinary fragmentation, uneven teaching capacity, and time–space limitations, are increasingly misaligned with human-AI collaborative learning. Against this backdrop, IP literacy must integrate legal knowledge, ethical judgment, compliance awareness, and AI-enabled creative practice. This study clarifies the renewed connotations of IP literacy in the AIGC era, develops a theoretically grounded model of influencing factors, and examines how multiple educational conditions combine to generate high-level outcomes. By focusing on IP literacy rather than generic digital competence, the paper addresses a clear gap in existing research and offers a configuration-based understanding that links theory to implementable strategies for intelligent, student-centered IP literacy education. [Method/Process] Grounded in Activity Theory, the study developed a six-dimensional framework consisting of the following variables: teacher professional competence, AI-IP awareness, diversified educational support, role division, evaluation mechanisms, and AI resources. These variables were operationalized via a structured questionnaire. Fuzzy-set Qualitative Comparative Analysis (fsQCA) was then employed to identify conjunctural causality and equifinal pathways that extend beyond linear models. High-outcome configurations were achieved through variable calibration, truth-table analysis, and minimization. Robustness was confirmed by tightening the PRI consistency threshold from 0.80 to 0.85. The path structure, overall coverage, and overall consistency remained stable. [Results/Conclusions] Findings show that AIGC-enabled IP literacy emerges through multiple effective configurational paths, rather than a single dominant factor. Across high-outcome configurations, teacher professional competence, AI–IP awareness, and diversified educational support consistently function as core drivers that shape learning processes and outcomes. Evaluation mechanisms and AI resources act as complementary or substitutive conditions, reinforcing effectiveness under specific institutional and resource constraints. Three typical paths were identified: a path emphasizing practice generation coupled with collaborative organization; a path that integrates resource sharing with practice-oriented development; and a path highlighting collaborative division of labor and effective communication to compensate for limited technical supply. Together, these paths confirm the internal logic of the six-dimensional model and demonstrate that coordinated configurations, rather than isolated improvements, are necessary to optimize IP literacy education in AI-rich contexts. Practical implications include strengthening AI-oriented teacher development, embedding AI-IP awareness in curricula and supporting services, building cross-unit collaboration mechanisms, and aligning role division and process evaluation with available AI resources. Although the cross-sectional design and limited scope constrain generalizability, the results provide a theoretically grounded and empirically supported basis for developing intelligent, collaborative, and student-centered IP literacy systems and offer a foundation for future longitudinal and comparative research in AIGC-enabled higher education.

[Purpose/Significance] In the era of widespread social media, network cluster behavior has emerged as a significant phenomenon that shapes online public opinion and collective action. Although existing research has thoroughly examined macro-level drivers and developed evolutionary stage models for network cluster behavior, there is still a significant gap in our understanding of the micro-level cognitive mechanisms that dynamically propel its evolution. Cognitive biases, which are inherent tendencies in human cognition, are amplified in online group interactions. This study specifically addresses this gap by adopting a cognitive bias perspective to investigate the evolution mechanism of network cluster behavior. It is crucial to focus on campus hot events as highly relevant and sensitive case studies. These events often involve students, parents, educational institutions, and the wider public, covering core issues such as campus safety, management disputes, teacher-student relations, and student rights. Their inherent emotional resonance, rapid dissemination within specific online communities, and potential for severe damage to reputation and social order necessitate deeper understanding. The core innovation and significance of this research lie in: 1) Systematically integrating cognitive bias theory to analyze the complete lifecycle evolution of network cluster behavior in campus events; 2) Empirically revealing how specific biases dynamically manifest and interact at various stages, shaping the trajectory of network cluster behavior; 3) Providing a richer theoretical framework for network cluster action theory; 4) Offering empirical evidence for formulating targeted governance strategies to mitigate risks associated with campus-related online crises, thereby promoting constructive online discourse and campus stability. [Method/Process] To rigorously investigate the core research question, this study employed the grounded theory methodology. Based on sustained high popularity rankings on the "Zhiwei Shijian" platform, ten representative campus hot events were systematically selected to ensure coverage of diverse campus issues. Extensive datasets of user comments related to these ten events were collected from the Sina Weibo platform, serving as the core empirical foundation. The data collection timeframe spanned the complete lifecycle of each event, from initial emergence to eventual subsidence. Following the grounded theory process, the collected textual data underwent a meticulous three-stage coding procedure to induce and refine textual themes. Through this process, facilitated by qualitative data analysis software, a substantive theoretical model was ultimately constructed. This model delineates the evolutionary path and internal mechanisms of network cluster behavior in campus events under the influence of cognitive biases. The grounded theory method was deemed highly appropriate due to its capacity for deeply exploring complex social processes and emergent phenomena directly from rich, context-specific data. [Results/Conclusions] The study found that the evolution mechanism of network cluster behavior in the context of campus hot topics mainly consists of five stages: public opinion induction, public opinion bias, public opinion diffusion, public opinion outbreak, and public opinion subsidence. Based on these findings, governance strategies for such campus network events have been proposed, including identifying triggering factors, avoiding cognitive biases, enhancing user literacy, promoting collaborative guidance, and mitigating secondary risks.

[Purpose/Significance] Open scientific data policies play a pivotal role in promoting the open sharing, unrestricted access to, and reuse of scientific data, thereby enhancing research efficiency and driving innovation. Despite their significance, research on the diffusion of these policies has predominantly focused on policy formulation, often neglecting the critical aspect of policy adoption and implementation at the local government level. This study aims to addres this gap by comprehensively examining the factors that influence the adoption of open scientific data policies by prefecture-level governments in China. The research was motivated by the need to understand how these policies spread across different regions, as well as the underlying mechanisms that facilitate or hinder their adoption. In doing so, the study expands the existing knowledge base by shedding light on the dynamics of policy diffusion in the context of open scientific data, a relatively under-explored area compared to other policy domains. [Method/Process] To achieve its objectives, the study employed an integrated research methodology. First, it utilized a policy diffusion model, adapted from the well-established Berry model, to theoretically frame the research. This model was enhanced by incorporating insights from a comprehensive literature review, which helps identify key internal and external factors influencing policy diffusion. Second, the study employed the event-history analysis to empirically test these factors using data from 286 Chinese cities over the period from 2018 to 2022. This method allows for the examination of the temporal sequence of policy adoption and the identification of causal relationships between the influencing factors and policy diffusion. Finally, a fuzzy-set qualitative comparative analysis (fsQCA) was applied to refine the understanding of multiple causal configurations that lead to successful policy adoption. This approach captures the complexity and interdependence of factors in policy diffusion processes, offering a nuanced perspective that goes beyond traditional statistical methods. [Results/ [Conclusions] The study identified four primary pathways for the diffusion of open scientific data policies in China: resource-driven, organization-and-human-capital-led, multi-stakeholder collaborative, and technology-guided. The resource-driven pathway emphasizes the significance of research funding and the establishment of professional organizations in facilitating policy adoption. The organization-and-human-capital-led pathway highlights the role of government official mobility and a skilled workforce in driving policy diffusion. The multi-stakeholder collaborative pathway underscores the importance of coordinated efforts among various stakeholders, including government agencies, research institutions, and industry partners. Last, the technology-guided pathway focuses on innovation capacity and professional management as key drivers of policy adoption. The findings reveal a heavy reliance on administrative measures in driving policy diffusion, which may lead to unintended consequences such as policy sustainability issues and a lack of alignment with local needs. Therefore, local governments are encouraged to adopt tailored diffusion strategies that consider their specific contexts and resource endowments. Future research should explore the performance of these policies in achieving their intended outcomes and conduct comparative studies across different regions to enhance the generalizability of the findings.

[Purpose/Significance] With the rapid advancement of artificial intelligence (AI), university libraries are undergoing a deep transformation from traditional resource repositories to intelligent service ecosystems. This transformation poses a significant challenge to the conventional competencies of librarians and underscores the necessity for a systematic reconstruction of these competencies. Existing studies often lack empirically supported and integrative models, and they seldom bridge the gap between AI application and competence development. To address these shortcomings, this study proposes a core competence model for hybrid AI librarians, integrating technical, service, and management dimensions. The research highlights its innovation by not only theorizing but also empirically validating the model through grounded data, positioning the study as a meaningful contribution to the discourse on digital librarianship. Different from previous literature, it integrates AI platform practices within the competency framework. This integration serves to enrich both theoretical underpinnings and enhance the practical applicability of the theory. This provides actionable implications for the sustainable development of librarianship in the context of national strategies for digital transformation and technological innovation. [Method/Process] The study employed a mixed-methods approach. First, a literature review was conducted to analyze trends in AI applications within university libraries. Then, semi-structured in-depth interviews were carried out with ten librarians from multiple universities that have deployed the DeepSeek intelligent platform. The participants covered technical, service, and management positions, with more than three years of experience using AI tools and a distribution across middle to senior professional titles. Following data collection, the grounded theory was applied with three levels of coding (open, axial, and selective) to inductively derive categories and explore how technical, service, and management competencies interact. The principle of data saturation was strictly observed to ensure methodological rigor, and no additional categories emerged after the three competency domains were established. [Results/ [Conclusions] Findings indicate that the core competencies of hybrid AI librarians revolve around three interdependent domains. Technical competence involves intelligent tool operation, data analysis, and system maintenance, supporting the integration of AI into daily workflows. Service competence emphasizes user-centered design, personalized recommendation, and human-AI collaborative interaction, ensuring that technical functions translate into user value. Management competence addresses resource allocation, cross-department collaboration, and ethical governance, safeguarding sustainability, compliance, and innovation. Together, these dimensions form a "technology-service-management" dynamic balance model, characterized by reinforcing loops in which technology drives service, service demands managerial support, and management stabilizes technology-service integration. Furthermore, a training and cultivation framework was proposed, offering differentiated professional pathways based on librarians' roles and growth stages. The study concluded that such a model not only enhances service effectiveness but also contributes to national innovation strategies. The study's limitations include its scope, which is limited to a single country and a small sample size. Future research should expand the sample base, employ comparative studies across institutions, and further examine the weighting of competencies.

[Purpose/Significance] In the current era, libraries are essential to fostering a reading-oriented society because they act as key hubs for disseminating knowledge. The goal is to increase public cultural literacy and foster an intellectual atmosphere. However, the lack of a professional framework for promoting reading in libraries severely hinders these efforts. Without clear standards, activities lack systematic planning, which leads to inefficiency and an inability to address diverse reading needs. This study systematically examines the professional services available to library reading promoters. By examining professionalization dimensions and influencing factors, it fills a research gap, enriches library science theory, and provides guidance for cultivating high-quality reading promotion teams. [Method/Process] In-depth interviews were used as the primary method to ensure research rigor. Fifteen participants were selected using purposeful sampling, including library scholars, experienced reading promoters, and front-line librarians. Each interview lasted between 50 and 70 minutes and covered the status of reading promotion, the challenges involved, and future expectations. Three stages of grounded theory analysis were then applied: open coding to extract initial concepts, axial coding to establish relationships between concepts, and selective coding to form a theoretical model. This data-driven approach validates the results. [Results/Conclusions] The research has achieved significant results by identifying three core dimensions of professionalization. For literacy specialization, reading promoters are required to have a solid grasp of library science, literature, and educational psychology. Training specialization emphasizes the establishment of a systematic training program that covers promotion skills, event planning, and user communication. A well-designed training system can continuously improve the professional capabilities of reading promoters. Reading promotion specialization focuses on adopting evidence-based and innovative strategies, which can enhance the effectiveness of reading promotion. Four influencing factors were also discovered: the curriculum system determines the content and quality of training; the resource system provides necessary physical and digital assets for reading promotion; the user service system affects the communication and interaction with readers; and the standardization system provides guidelines for the evaluation of reading promotion work. Based on these findings, practical suggestions were put forward, including optimizing the training model by combining theoretical learning with practical operation and establishing a standardized management system for reading promotion. Nevertheless, the study has certain limitations, primarily due to its relatively small sample size. Future research could expand the scope of the sample, conduct long-term follow-up studies on the impact of professionalization, and explore integrating emerging technologies such as AI and big data into the professionalization of reading promotion to further promote the development of library reading promotion services.

[Purpose/Significance] The global population is aging at an unprecedented pace. As a key tool to address the challenges of digital inclusiveness for the elderly, developing a digital capital scale is of utmost importance. Digital capital not only encompasses the abilities and skills of the elderly in using information technology, but also focuses on the interaction among the social resources, cultural capital, and economic capital they acquire in the digital environment. Therefore, it helps enhance the theoretical understanding of the heterogeneity of the elderly's digital capabilities. [Method/Process] First, a semi-structured interview method was adopted to conduct in-depth interviews with 24 elderly individuals based on the digital capital framework, and combined with the digital life scenarios in China. We also referred to existing studies on the digital literacy and digital capabilities of the elderly. Based on the coding results of the interview transcripts, a 7-dimensional scale for measuring the digital capital of the elderly was derived. Then, a preliminary reliability and validity analysis was conducted on a pre-test sample of 180 respondents, and the dimension indicators were appropriately adjusted. Subsequently, using the data from 380 formal questionnaires, the scale was verified and improved. Based on the principle of conceptual interpretability, the factor names of the four dimensions were re-examined, and the final version of the scale was established. Elbow estimation and the K-means clustering algorithm were then used to classify the digital capital levels of the elderly. [Results/Conclusions] The final scale consists of 19 items, covering four dimensions: digital resource acquisition ability, digital creation and expression ability, digital environment adaptation ability, and digital tool learning ability. Following optimization, the scale demonstrates excellent reliability and validity, and aligns closely with the aging-friendly scenarios. The tool can be used as a standardized tool to measure the digital capital level of the elderly population in China, laying the foundation for future large-scale surveys. By applying this scale, it is possible to effectively distinguish between groups of elderly individuals with varying levels of digital capital, providing empirical support for personalized digital services for the elderly people. For the first time, this study systematically applies the digital capital theoretical framework to the elderly population, which compensates for the lack of standardized measurement tools and highlights the unique needs and challenges of the elderly in terms of the dimensions, usage scenarios, and capability transformation. The proposed hierarchical model of digital capital among the elderly deepens our theoretical understanding of the differences in digital capabilities among this population.

[Purpose/Significance] Starting from the perspective of technological complementarity, this paper proposes a new approach for identifying technological opportunities by comprehensively using outlier patents and hot patents. The fusion analysis of innovative outlier patents and market mature hot patents is carried out to identify "innovation maturity" technological opportunities that combine innovation and maturity, which is of great significance for enriching the theory and methods of technological opportunity identification. [Method/Process] First, based on the "association distribution" characteristics of patent classification numbers, a two-stage method was adopted to screen patents. In the first stage, we used the association rule algorithms to find classification numbers with weak and strong associations, and obtained initial outlier patents and initial hotspot patents. In the second stage, outlier detection algorithms were used to obtain the marginalization classification numbers of the two types of patents in the first stage. Patents containing marginalization classification numbers were selected as the final outlier patents, while patents containing such classification numbers were removed as the final hotspot patents. Second, different methods were adopted for patent screening based on the differences in innovation and maturity of patent content. Using structured and unstructured data from patent databases, we constructed time weighted indicators and keyword uniqueness indicators as the screening indicators for innovative outlier patents. We constructed a technology lifecycle stage discrimination function and patent market value measurement indicators as the screening criteria for mature hot patents in the market. The screened patents were classified into technical fields based on the major categories in the International Patent Classification. Finally, we identified technological opportunities based on technological complementarity. By using the generative topology mapping algorithm to obtain a technical blank point map, the top K keywords in each blank point were obtained, and the sources of the keywords were marked to ensure that new technological opportunities have both good innovation capabilities and mature market prospects. In the future, keyword combinations derived from different types of patents were regarded as "innovation mature" technological opportunities. [Results/Conclusions] Taking the field of new energy vehicle batteries as an example, empirical analysis was conducted to obtain a total of 10 technical opportunities in 5 sub technical fields. Through content comparison with relevant policy texts, 7 technical opportunities showed high consistency. It was found that the identification results were highly consistent with the current technological layout and development direction of the field, indicating that this method has good effectiveness and scientificity in technology opportunity identification, and can provide support for technology prediction and innovation decision-making.

[Purpose/Significance] With the rapid advancement of artificial intelligence (AI) technologies, libraries are transforming their service models and content offerings. Large AI models have opened up broader development opportunities for smart libraries. However, the rational adoption and application of these models has posed a significant challenge to libraries. This study employs multimodal resource profiling to conduct research on the optimization of large AI model utilization in libraries, revealing the intrinsic relationships among various types of library resource data. Based on these insights, the optimization methods and related strategies are extracted to enhance the efficiency of library resource utilization and improve user experience. [Method/Process] Multimodal resource profiling is a comprehensive representation that captures the intrinsic characteristics of library resources through tag extraction, aggregation analysis, and visualization of diverse data generated within the libraries. By utilizing a novel clustering algorithm, it overcomes the high sensitivity to input parameters characteristic of traditional algorithms and achieves natural clustering across resources with varying densities, thereby enabling the generation of accurate multimodal resource profiles. The resource profiling model provides a theoretical foundation for optimizing the deployment and utilization of large AI models in libraries, while also delivering rich data support for subsequent AI model applications. The adoption strategy proposed in this study is divided into two aspects: model selection and model utilization. Model selection focuses on compatibility and accuracy to achieve an optimal match between the model and both library resources and user needs. Model utilization emphasizes the effectiveness and usability of the output, thereby enhancing operational efficiency and user experience. Based on this framework, the overall operational mechanism of the adoption optimization strategy is designed around continuous model monitoring, real-time collection of user feedback, iterative model updates, and dynamic adjustment of multimodal resource profiles. [Results/Conclusions] This study takes a public digital library on "Telegram" as a case study to generate multimodal resource profiles, which meticulously categorize user groups, interests, and emotional intensities. By integrating the large AI model adoption optimization strategy with the outcomes of multimodal resource profiling, the model autonomously identifies the most task-relevant features, reducing the need for manual intervention. Not only does it achieve high prediction accuracy, but the explanatory feature weights it outputs also provide a quantifiable basis for service optimization. Through comparative experiments with commonly used structural modules, the proposed method demonstrates significant advantages over traditional recommendation systems in terms of both resource utilization efficiency and user engagement. This study lays the foundation for the future development of library technology and opens up new possibilities for the application of multimodal resource profiling.

[Purpose/Significance] In today's knowledge economy, where scientific research and innovation drive social change, accurately and scientifically assessing the social impact of scientific research achievements has become key to optimizing the global scientific research ecosystem. This article focuses on the social impact evaluation system of the international scientific research achievement. It provides in-depth analysis of typical international models and strategic guidance for China to build a more comprehensive and efficient evaluation system. [Method/Process] Based on the theoretical definition of the social impact of scientific research achievements, eight major cases of third-party evaluations were selected: the EU SIAMP, the US STAR METRICS, the UK REF, the Dutch SEP, the Italian VQR, the Canadian CAHS, the Australian ERA, and the Japanese NIAD-QE. Using a cross-national comparative analysis method, a comprehensive analysis was conducted across three dimensions: system elements (establishment time, establishing entity, main characteristics, evaluation scope, and strategic objectives), mechanism processes (definition of evaluation objects, establishment of evaluation procedures, application of evaluation results), and methodological tools (definition of social impact-related content, evaluation methods, and indicator content). Subsequently, relevant information was collected through literature research and online research to identify key characteristics. [Results/Conclusions] International evaluation systems are guided by national strategic needs and incorporate social impact into the entire research lifecycle management process through legislation. These systems also link influence to funding allocation. These systems operate using policy-driven mechanisms, collaborative efforts among stakeholders, data-driven methodologies, and dynamic feedback loops. The key characteristics of typical international research evaluation models are as follows: 1) Multi-dimensional indicators: Moving beyond traditional academic metrics, evaluation frameworks now encompass a wide range of impacts, including the effects of research outcomes on social welfare, industrial development, and employment. 2) Dynamic adjustment: As the socio-economic and technological environment evolves, the social impact evaluation systems of international research outcomes also undergo dynamic adjustments and innovations. 3) Multi-stakeholder collaboration: This involves diversified participation, cross-disciplinary and cross-departmental collaboration, and the full involvement of stakeholders throughout the process. Based on the above findings, this study offers insights at different stages of social impact assessment of scientific research achievements. Prior to implementation, additional indicators aligned with domestic strategic priorities, such as environmental sustainability, social equity, and cultural heritage preservation, should be incorporated alongside traditional metrics, and the policy and legal framework should be refined. During implementation, a multi-stakeholder collaborative evaluation platform should be established, and a dynamic system incorporating resilience coefficients should be developed to address uncertainties. After completion, a long-term monitoring and tracking mechanism should be implemented to understand ongoing impacts, with feedback-driven updates to the indicator system. This approach aims to foster a healthy evaluation ecosystem, accelerate the translation of research outcomes into societal value, and promote the integrated development of scientific research and social progress.

[Purpose/Significance] Under the background of digital government construction, as a new type of service subject of human-machine collaborative governance, the influence mechanism of the social role positioning of government digital humans on public adoption behavior urgently needs theoretical exploration. Most existing studies have focused on the technical level. This study, based on the perspective of social role theory, explores the influencing mechanism of different role positioning of government digital humans in government service scenarios on public adoption behavior, which is of great significance for optimizing government services and improving the intelligent level of government services. [Method/Process] An experimental research method was adopted to construct a two-factor inter-group experimental design of "social role-business type", and a simulation experiment of government service scenarios was carried out through random grouping. Based on previous studies, we defined the role positioning of "advisors" and "decision-makers" for government digital humans, and constructed experimental scenarios by combining two service scenarios of consultation and approval. The subjects were randomly grouped to complete the role cognition test and human-computer interaction tasks. Data were collected by using the research path combining situation simulation and questionnaire survey. The psychological mechanism and decision-making logic of the public's adoption behavior were analyzed through the data analysis results. [Results/Conclusions] The research findings are as follows: 1) There is a significant interaction effect between the social roles and business types of government digital humans. In approval service scenarios, the decision-maker role is more capable of promoting public adoption behavior than the advisor role; 2) Human-computer trust perception plays a crucial mediating role in the influence path of social roles on the public's adoption behavior, revealing the core value of the trust mechanism in human-computer interaction; 3) The synergy effect between role authority and task fit constitutes an important mechanism influencing public cognition. This study expands the explanatory boundary of the social role theory in the field of intelligent government services and provides theoretical support for the construction of smart government services. However, there are still certain limitations. The service scenario simulation in the experimental design is difficult to fully restore the complexity of real government services. Future research can extend the multi-dimensional role classification system and deepen the mechanism exploration by combining the mixed research method. We have verified the applicability of the theoretical model in real government service scenarios and expand the existing conclusions. In addition, exploration on the dynamic impact of long-term interaction between government digital humans and the public on behavioral evolution is also a potential research direction.

[Purpose/Significance] The ongoing digital transformation has led to significant changes in public cultural services, particularly in content generation, communication channels, and modes of public participation. "Accessibility," a key indicator of the extent to which citizens' cultural rights are realized, is typically assessed along four dimensions: availability, acceptability, accessibility, and adaptability. Previous research has focused primarily on the supply side of accessibility, examining how factors such as the distribution of cultural resources, infrastructure development, and policy support affect user engagement. However, with the widespread adoption of digital technologies, individuals' ability and willingness to access information, utilize services, and provide feedback - collectively referred to as "digital literacy" - has become an increasingly important variable influencing cultural participation. Consequently, this study seeks to explore the relationship between users' digital literacy and the accessibility of public cultural services from a demand-side perspective. It aims to provide a more systematic theoretical framework and practical approach to optimizing the effectiveness of public cultural services. [Methods/Process] This study assesses users' digital literacy by examining their level of digital access, Internet usage, and service availability based on data collected from the Beijing-Tianjin-Hebei region. A structured questionnaire yielded 892 valid responses. To analyze the relationship between users' digital literacy and the accessibility of public cultural services, the study applies a generalized ordered logit model. A generalized ordered logit model is employed to analyze the substitution and overlap effects between users' digital literacy and the various dimensions of service accessibility. [Results/Conclusions] There is currently a digital divide exists between different demographic groups. A significant substitution effect is observed between traditional public cultural accessibility and users' digital literacy, with limited overlap between the two. Digitization has driven the modernization of public cultural resources and services, particularly in terms of technology and service delivery. However, there remains a time lag between the users' digital literacy of users and the digital transformation of the public cultural supply side. This lag suggests that the digital needs of users and the availability of digital cultural services are not fully aligned, which negatively impacts the effectiveness of public cultural services. Therefore, enhancing users' digital literacy, especially improving their ability to adapt to digital cultural resources, is a crucial factor in transitioning public cultural services from "accessibility" to "enjoyment". In promoting the digital upgrading of public cultural services, greater emphasis should be placed on developing users' capabilities and anticipating their needs.

[Purpose/Significance] The development of artificial intelligence generated content (AIGC) technology has engendered novel prospects for the establishment of creating inclusive and expansive learning environments. In light of the potential risks associated with the misuse of AIGC tools, the present study analyzes the factors influencing students' use of AIGC tools within the context of artificial intelligence literacy. It constructs a conceptual model framework and explores the relational paths among influencing variables, aiming to provide a theoretical basis for the advancement of AI literacy education in libraries and other educational institutions. [Method/Process] This study adopts a mixed-method approach that primarily integrates Structural Equation Modeling (SEM) and mediation analysis to explore the relationships between the factors that influence AIGC tool usage. A conceptual relationship model was constructed based on the Technology Acceptance Model (TAM), which is widely utilized model for assessing users' acceptance of new technologies. The study builds on this model by adding AI literacy as a key variable to examine its moderating role in shaping the students' use of AIGC tools. The data were collected via a survey disseminated to university students who have used AIGC tools. The survey incorporated a series of inquiries designed to assess constructs such as effort expectancy, performance expectancy, behavioral intention, AI literacy, and actual usage of the tools. The SEM approach was employed to assess the proposed hypotheses and to validate the relationships between the identified factors. Mediation analysis was employed to assess indirect effects between variables. [Results/Conclusions] The findings indicate that effort expectancy exerts a direct impact on the actual use of AIGC tools by students, and indirectly promotes usage behavior through performance expectancy and behavioral intention. Furthermore, AI literacy plays a crucial role in improving the conversion rate from intention to actual usage. Specifically, AI literacy significantly enhances students' acceptance of AIGC tools, especially in terms of increasing their practical ability to use these tools effectively. The research also identifies key factors that influence students' use of AIGC tools, such as performance expectancy, effort expectancy, and behavioral intention, and highlights the significant moderating effect of AI literacy on the relationships among these factors. This study provides empirical evidence for the effective integration of AIGC technology into the education sector and offers theoretical guidance for libraries and educational organizations on how to design AI literacy education programs that help students adapt to a digitally driven society. Future research may encompass a more extensive examination of the utilization of AIGC tools across different academic disciplines, with a particular emphasis on their implementation in specialized domains. Additionally, the proposed model may be refined to better accommodate a wider range of educational contexts and learning scenarios.

[Purpose/Significance] Research on the behavioral stimulation of online social platform interactions triggered by IP-based games on users' active learning and reading, the social demands within gaming communities and their derivative reading-sharing interactions constitute dual intrinsic motivations that promote autonomous reading behaviors. Exploring new developmental directions for reading promotion through digital game dynamics and group-based social guidance provides broader research perspectives for innovative knowledge acquisition and pedagogical learning paradigms. [Method/Process] Based on the game "Black Myth: WUKONG" as the research background, we collected relevant comments and original texts from four social media platforms. Using the LDA model for topic classification of the effectively collected data, users who demonstrated marked behavioral tendencie towards book-related engagement and reading activities attributable to the "WUKONG" game experience were manually identified from the aforementioned dataset. We explored the details of their social discourse and the behavior of user accounts through user-account backtracking, studying the factors that stimulate users' interest in reading and active reading behavior. [Results/Conclusions] Analysis of the five thematic clusters identified by the LDA modeling revealed that in the user behaviors focused on the theme of cultural exploration, 38.3% of the user behavior data showed increased exploratory engagement with original literary works and related content during "WUKONG" mediated group interactions. Whether this interactive exploration closely connects to reading habits needs further study. Further research has shown that a portion of users were influenced in their subsequent behaviors by this game and social interaction. Through text mining of user content on key topics, analysis revealed that 61.15% of user accounts had no prior engagement history, representing first-time participants in the cultural learning interactions of the "WUKONG" game. Notably, 23.7% of this cohort spontaneously expressed self-directed reading intentions during the game-social scenario. As the dominant subgroup in the dataset, their behavioral patterns suggest that gamified social platforms may serve as critical trigger mechanisms. It was found that the factors that stimulate users to read independently include competing for the right to speak in social interactions and obtaining gaming experiences. Accordingly, strategic practices for autonomous reading should accordingly be implemented through digital content guides, transmedia narrative interactions, and visual scene experiences. This research investigates the orienting mechanisms of digital games and community interactions in edutainment convergence, demonstrating both theoretical value and practical implications for user behavior analysis and reading promotion. While the study design ensured breadth of data collection, the heterogeneity of social attributes across platforms warrants further investigation. Subsequent studies should conduct platform-specific comparative experiments to strengthen the empirical foundation for behavioral intervention strategies.

[Purpose/Significance] To address the problem of semantic ambiguity and soybean breeding knowledge that needs to be revealed in depth, a structured knowledge model was established to thoroughly discuss the definition of key concepts and their interactions involved in the breeding process, standardize the definition and organization of soybean breeding knowledge, and promote the unified expression of knowledge. [Method/Process] By analyzing the characteristics of knowledge structure in the field of soybean molecular breeding, according to the seven-step method of Stanford ontology construction, the semantic model of soybean molecular breeding was established by using the ontology construction tool protege 5.6.3. A total of 48 classes were constructed in the soybean breeding concept ontology, which clarified the concepts and hierarchical associations among concepts under traits, compounds, enrichment pathways and growth classification. Seven types of causal relationships and three types of static relationships were defined. Finally, the ontology-based knowledge graph was presented based on a PubMed literature, and the knowledge unit with Dt1 gene as the central node was queried. [Results/Conclusions] This study integrated the existing knowledge base and ontology related to soybean breeding, established a knowledge model at the biomolecular level in the field of soybean breeding, and provided a certain reference for knowledge sharing and semantic integration in this field. Compared with the existing knowledge models, this study analyzed the characteristics of knowledge structure in soybean breeding, extracted the key entity types and relationship types in the process of hypothesis generation, and constructed an ontology model based on this, which could describe gene expression patterns in soybean growth and development more comprehensively. This is of great significance for discovering the key genes associated with specific traits and analyzing the molecular regulatory networks formed by traits, which will help to accurately design and optimize breeding strategies. The knowledge model constructed in this study could be applied to knowledge discovery, causal reasoning and other scenarios in soybean breeding, supporting experimental design and promoting interdisciplinary communication. The limitation of this study is that the ontology was constructed manually and no automated natural language processing method was used. In addition, in the subsequent use of soybean breeding knowledge model, it is necessary to keep up with the frontier of development in soybean breeding, expand new concept types, add new concept names and relationship names in time according to the knowledge description needs of field scientists, and regularly maintain and expand soybean breeding knowledge model.

[Purpose/Significance] With the rapid development of international academic digital publishing, ebook sales have surpassed those of print books since 2020. Many academic libraries in the USA are adopting e-preferred book collection development policies. In recent years, China's academic ebook market has been growing rapidly, as the well-known academic publishers such as Science Press and Tsinghua University Press have successively launched their own ebook platforms, and high-quality ebook integrator platforms such as Keledge and Cxstar have emerged, leading to a qualitative leap in both the quality and quantity of Chinese academic ebooks. The rapid development of the Chinese and English ebook markets has made collaborative collection development of print and ebooks feasible, which is considered to be a good solution to the tight collection budget and insufficient library space for Chinese academic libraries. The paper aims to propose a strategic direction and implementation path for collaborative collection development of print and ebooks, and provide a reference for the high-quality development of academic library collection development under the background of "Double First Class" construction. [Method/Process] The paper summarizes the main characteristics of SUSTech Library's three phases of book collection development, and introduces SUSTech Library's collaborative collection development practices of print and ebooks from four aspects: collaborative acquisition, collaborative management, collaborative services, and collaborative evaluation. A collaborative acquisition strategy should be based on four factors: collection assurance, patron needs, library space, and cost effectiveness. Collaborative management should redesign the business process from the aspects of funding plan, book selection, acquisition implementation, metadata management, and statistics. Collaborative services should be based on the needs, recommendations, and usage of user groups and individual users. Collaborative evaluation refers to a structured analysis on the print and ebook collections across five dimensions, including language, subject, publisher, book type, and year of publication. [Results/ [Conclusions] The paper puts forward suggestions from three aspects, such as top-level design, deep integration of business processes, and integrated management platforms. The academic libraries' management team should develop collaborative collection development policies and strategies from the top down. Deep integration of business processes should support optimization of organizational structure and job position adjustment, which replaces the traditional approach of defining job positions and responsibilities based on a single material type or business process. Integrated management platforms should support the integration of multiple metadata sources and business processes, and have strong knowledge discovery and service capabilities.

[Purpose/Significance] As part of the plan to build a better new digital life, information literacy (IL) is a survival skill in the information age. As a major repository of information resources, public libraries are the backbone of public IL education. The research on readers' knowledge construction behavior of IL education in public libraries is not only helpful for readers to improve their individual IL and self-learning ability, but also constructive for the development of the national IL system. At present, relevant research mainly focuses on the framework and practical suggestions of IL education, and rarely analyzes the construction of readers' knowledge in IL education from the perspective of knowledge management and readers' behavior. [Method/Process] From the perspective of readers' knowledge construction, this paper logically extends to the necessary knowledge scene transformation in public library IL education, and finally rises to the level of development strategy on how readers can realize knowledge construction and innovation in public library IL education. Information literacy education in public libraries also includes explicit and implicit knowledge. Information literacy education activities in public libraries focus on sharing explicit knowledge, enhancing the value of collection knowledge, but more attention should be paid to the exploration of implicit knowledge, enhancing knowledge transformation and innovation of participating readers. The path of reader knowledge construction in IL education can be divided into individual knowledge, team knowledge, organizational level knowledge. According to the different levels of knowledge construction, four knowledge transformation modes constitute the spiral process of knowledge innovation. Based on the MOA model, this paper analyzes the motivational factors, opportunity factors, and ability factors of readers' knowledge construction in the IL education in public libraries, as well as the constituent elements of the knowledge construction community and their interactive relationships from a multi-dimensional dynamic perspective. The model of readers' knowledge construction in public library IL education from the perspective of MOA analyzes the influence of motivational factors, opportunity factors, and ability factors on the readers' knowledge construction behavior in the knowledge construction community of IL education. Motivation points to opportunities and skills, suggesting that motivation leads readers to seek opportunities and develop necessary skills; motivation, opportunity and ability, and knowledge construction community all point to readers' knowledge construction behavior, suggesting that together they influence and promote readers' knowledge construction process. [Results/Conclusions] Readers' knowledge construction in the MOA model is a dynamic and multi-stage process. In the IL education activities of public libraries, readers gradually promote their understanding of knowledge and apply it to practical situations by stimulating motivation, exploiting opportunities, improving skills, and ultimately constructing knowledge. At the same time, through the feedback evaluation stage, further enhance knowledge construction strategies, update development goals, and continue reform and innovation. This study is only a theoretical extension of practical work experience, and does not involve rigorous and formal data verification and case study. In the next step, the questionnaire will be used to collect sample data, and through hypothesis testing and model fitting, the application practice of the MOA model in readers' knowledge construction behavior will be further verified.

[Purpose/Significance] Artificial intelligence-generated content (AIGC) is having a profound impact on the field of education. Currently, there are some problems in the digital education environment, such as incomplete digital infrastructure and slow digital transformation. The postgraduate education system has not yet fully responded to the changes in the educational environment in the intelligent era. In the era of AIGC, digital literacy has become an important component of graduate students' core competence, which is related to their future academic research and career development. In order to promote graduate students from the understanding of intelligent technology to the rational application, this paper explores a new way of talent training to adapt to the development of intelligent technology. Improving graduate students' literacy skills is important for adapting to the new demands of learning and research in the AIGC era. [Method/Process] Through a literature review and case analysis, this study explores the importance of digital literacy education for postgraduate students, and identifies challenges in educational content and teaching methods. Based on the successful experience of international universities, by analyzing the advantages and application scenarios of AIGC technology, combined with the current situation and existing problems of graduate students' digital literacy education, this paper proposes strategies and ways to improve graduate students' digital literacy. Based on the relevant theories of educational technology development, combined with educational practice and case analysis, this paper proposes an improvement plan for postgraduate students' digital literacy education in the AIGC era. [Results/Conclusions] In order to adapt to the changes brought about by AIGC technology, colleges and universities need to innovate in curriculum design, teaching paradigm and evaluation methods, and put forward strategies such as introducing AIGC-related knowledge modules, building interactive digital resources' intelligent recommendation platform, establishing interdisciplinary integration mechanism, strengthening ethical and legal education and establishing supervision mechanism, so as to promote the comprehensive ability of graduate students. Future research can further explore the deep integration path of AIGC technology and postgraduate students' digital literacy education, the high-order thinking practice direction of AIGC to promote digital literacy, and how to give full play to the positive role of AIGC technology in education while ensuring academic integrity. The shortcoming of this study is that because the development of AIGC technology is still in rapid evolution, some of the suggestions of the study may need to be adjusted in time according to the further development of the technology.

[Purpose/Significance] Limited by the constraints of traditional literature classification systems, scientific and technical literature information resources face problems such as inadequate disclosure and resource utilization. At the same time, high-quality user-generated data cannot yet be integrated as data elements into services related to scientific and technical literature services, which prevents these services from adapting to the context of the open science and meeting the diverse knowledge needs of readers. This study aims to harness the technological breakthrough potential of AI to build a consumer-end data system for scientific and technical literature information resources driven by AI and experts. This will help to overcome the shortcomings of traditional services, such as the lack of supporting reading information and low interactivity between users, with the hope of promoting the optimization process of scientific and technical literature information resource services. [Method/Process] First, the study analyzes the four-dimensional value representation of the consumer-end data systems for scientific and technical literature information resources, including the intrinsic value, the tool value, the academic value, and the future value of annotation data. Then, following the processing flow of consumer-end data, namely the collection phase, utilization phase, and management phase, the paper proposes principles for the construction of consumer-end data systems. Furthermore, the paper deconstructs and analyzes the risks associated with the involvement of AI in the construction of consumer-end data systems, including four types of risks: machine algorithm risks, annotation content risks, annotation data risks and application risks. Finally, based on the degree of AI involvement in data annotation work, three innovative models of AI plus expert collaborates with user to accomplish data annotation for scientific and technical literature information resources are designed: the AI plus expert-assisted data annotation model, the AI plus expert collaborative data annotation model, and the AI plus expert-led data annotation model. [Results/Conclusions] Under the AI plus expert-assisted data annotation model, AI acts as a tool to complete surface-level information processing based on rules set by experts to assist users in data annotation. In the AI plus expert collaborative data annotation model, AI completes the review of pre-annotation tags for scientific and technical literature information resources, transforming users from a self-generated tag mode to an AI-generated data tag evaluation and selection mode, with experts assisting in the final review of data tag quality. In the AI plus expert-led data annotation model, users provide data annotation requirements, experts guide the process, and data annotation is automatically completed by the AI4S platform.

[Purpose/Significance] Improving the policy and governance systems to promote the development of strategic industries such as artificial intelligence was explicitly proposed in the resolution of the Third Plenary Session of the 20th Central Committee of the Communist Party of China. In recent years, the conflict between AI companies' desire for copyrighted data and the copyright holders' protection of copyrighted data has become increasingly apparent. There have been a number of lawsuits and disputes around the world regarding copyright infringement caused by artificial intelligence. The dilemma of copyright protection of AI training data has become a difficulty and bottleneck that urgently needs to be resolved in the development of high-quality data system for AI. [Method/Process] Based on the academic research and industrial practice on the copyright protection of AI data, this study systematically summarizes six representative approaches to address the copyright dilemma of AI training data, and provides a comparative analysis of the advantages, disadvantages, and applicability of these approaches. The six representative approaches are: signing a license agreement by both parties, initiating special plans or forming alliances, introducing a copyright notice mechanism, introducing a copyright risk guarantee mechanism, replacing with synthetic data, and applying copyright detection tools to large language models. For the copyright dilemma of AI training data, there is no optimal solution that can both encourage the supply of AI copyright training data and protect the copyright of data. [Results/Conclusions] In order to provide helpful references for increasing the supply of AI copyright data, formulating relevant policies, and promoting related work, this study has proposed a concept of general implementation path to build a high-quality data system for AI to solve the copyright dilemma of AI training data, based on the comparative analysis of the above six representative approaches and combined with China's four unique advantages. These include: 1) Integrating existing platforms to build a national-level integrated service platform for copyright data for AI, with state-owned enterprises (SOEs) under the direct administration of the central government taking the lead in establishing a national copyright data alliance and connecting copyright data to the platform. 2) To collaborate with local pilots of data intellectual property rights, explore and promote comprehensive reform pilot programs of copyright data adapted to the development of AI, and continuously strengthen the cooperation efforts and willingness between AI enterprises and copyright holders. 3) The focus should be on principled or critical issues, establishing and improving legislation related to copyright data for AI and promoting industry self-regulation.

" AI for Science " (AI4S) is a new scientific research paradigm that deeply integrates AI technology with scientific research to promote the discovery of new knowledge and the solution of scientific problems. As the application of AI4S in the natural sciences and humanities and social sciences advances, its development line, opportunities and challenges, needs and tasks, and ways of realization deserve further discussion. In order to advance AI4S research, promote scientific and technological (S&T) innovation and progress, and facilitate the effective strengthening of the discipline of information resources management, our journal has invited seven experts to organize this academic conversation on AI4S. 1) Supporting knowledge services for AI4S: In the current landscape of intelligent knowledge services, the requirements for supporting AI4S have increased, including the need for multi-level knowledge discovery and acquisition, cross-disciplinary research and innovation, and user-friendly participatory services. In addition, knowledge service scenarios are moving towards diversification, complexity, depth, specialization, and personalization in ubiquitous knowledge discovery, generative content services, and multi-round interactive service exploration. In response, professional science and technology information organizations need to reassess the role of knowledge services in the AI4Science environment and their significance in comprehensively supporting the S&T innovation process. This involves establishing a broad literature perspective, deepening full-text knowledge elements, balancing universal and specialized depth, autonomously developing core products, and deeply engaging with professional fields to support interdisciplinary innovation. 2) As a knowledge base for AI4S: In the development of AI4S, S&T literature serves as a high-quality corpus of great importance and utility. The Documentation and Intelligence Center of the Chinese Academy of Sciences has developed the concept and general framework for an AI4S knowledge base utilizing S&T literature. It is dedicated to building four types of knowledge bases to support intelligent services such as evidence-based retrieval, situational awareness, inference prediction, and insight generation required for AI4S applications. In addition, to advance the AI for Science knowledge base, it is essential to actively promote the construction of an intelligent data system, develop an AI engine for technical literature knowledge, conduct key technology research on in-depth mining and intelligent analysis of S&T literature, and promote collaboration with scientific research units across various fields, leading AI companies, and teams of field scientists. This approach aims to fully exploit the innovative and developmental value of the discipline of information resource management. 3) Powering AI4S with scientific data: Effective aggregation of scientific data is the foundation for unleashing the powerful capabilities of AI4S. This is essential for libraries to adapt their roles and functions in the AI era and is a crucial prerequisite for catalyzing the transformation of scientific research services, deepening scientific research support, and accelerating S&T innovation. Currently, libraries face various macro and meso challenges in effectively aggregating valuable scientific data to provide support for AI4S. To address these challenges, the following ways can be pursued: defining the roles and functions of libraries in scientific data management; promoting a conducive environment for scientific data management; establishing a collaborative network for scientific data management; and enhancing the service capacity of scientific data management. 4) AI4S and intelligent language modeling for classical literature: AI4S technology can be used to analyze documents and texts, enabling a faster and more comprehensive understanding of a vast amount of historical documents and cultural materials. The development of intelligent language modeling for classical literature represents a significant breakthrough in the field of ancient literature research, bringing new opportunities and challenges. With the increasing popularity of multimodal and generative GPT models in the context of AI4S, the intelligent language modeling of classical literature will focus on integrating diverse information, enhancing adaptability, improving knowledge representation, and addressing a wider range of application scenarios. 5) Library Digital Scholarly Services for AI4S: The concept of using LLM-based AI4S and AIGC to drive the development of smart libraries is consistent with the vision for digital scholarly services in libraries, and presents both opportunities and challenges. Given the trends towards AI4S platformization and the characteristics of "middle-end" digital scholarly service, as well as the longstanding tradition of libraries in serving scholarly research, the reengineering path for the library's digital scholarly services platform includes three approaches: building an AI4S service platform independently, purchasing and utilizing third-party AI4S platforms, and promoting embedded knowledge services as a component of scientific intelligence. This innovative approach addresses the dilemmas of financial resources, human resources, cognitive and practical gaps, and emphasizes the importance of user needs in the AI4S environment. It also focuses on knowledge organization and service delivery to meet user needs in the AI4S landscape. 6) Historical evolution and logical structure of the scientific intelligence paradigm (AI4S): AI4S is a scientific paradigm change dominated by the full application of AI technology to various disciplines, and its logical structure includes "data+model"-driven, knowledge ecology created by machine conjecture, and application scenarios expanded by algorithmic thinking. In the era of digital civilization, AI4S-driven scientific progress and social development must carry forward the value of science and technology for the good, effectively select the theoretical arguments and proposals for extending AI4S to the field of social sciences and humanities, and improve the series of mechanisms for integrating human decision-making and machine intelligence. 7) Development opportunities and prospects of AI4S in the era of generative AI: With the advances in generative AI, pre-training algorithms and large-scale pre-trained models have provided significant opportunities for AI4S in various disciplinary domains. These technologies have shown immense potential and value for applications in diverse fields such as industrial inspection, robotics, and medicine. Additionally, it is crucial to emphasize the importance of key factors such as the constraints of technical implementation conditions for large pre-trained models, the sustainability of data/computing resources, and the transparency, fairness, and accessibility of the technology.

[Purpose/Significance] The continuous development of metaverse technology marks the transition of mankinds from information society to information civilization. How to understand the relationship between human beings, physical space and information space in the future society has become a key problem of the era. The mixed reality (MR) Technology is a new intelligent technology that integrates the advantages of augmented reality (AR) and virtual reality (VR), makes virtual objects coexist in the physical world, and integrates the functions of human perception, computer processing and environmental input. The new generation of MR technology has improved the traditional global understanding of digital reality interaction, and also has been bringing technological innovation opportunities for the development of smart libraries. Exploring the new application scenarios of MR technology is helpful in expanding the depth and breadth of the research on smart libraries. [Method/Process] By using the methods of literature review, content analysis and website analysis, this paper reviews the current research status of MR Technology in the field of library science at home and abroad. In addition, through practical cases, this paper summarizes the relevant experience and existing gaps in the application of MR Technology in domestic and foreign libraries. Therefore, it is clear that the research of this paper aims to further stimulate and release libraries' demand and potential for MR Technology. Specifically speaking, by examining the characteristics of high realism, more intelligent and omni-directional MR technology, this paper further explores the ability of smart libraryies in four dimensions of service, knowledge, experience and collaboration, which will contribute to building a new application scenario of smart libraries with the vision of MR technology. It is hoped that this paper can promote the formation of a new type of smart libraries that combines dynamic and static, actively data, blending virtual-real and multi-dimensional expansion. [Results/Conclusions] In the wave of rapid innovation of VR, the construction of smart libraries should be considered in four dimensions: problem orientation, theoretical supports, talent management and subject co-creation. It can provide a better understanding of the future smart libraries with the possible risks, urgent internal and external needs. It is expected to build a future ecological picture of the integration of smart libraries and MR technology. However, due to the limitation of the author's knowledge level and the lack of practical ability, this paper provides a relatively macro guidance. Libraries vary in their application of MR technology. On specific issues, we need specific analysis and different solutions. Therefore, in the future research, we will continuously improve and refine the research in this aspect, and provide reference basis and application value for the effective practice of MR technology in the smart libraries.

[Purpose/Significance] Investigating the influencing factors in the general requirements of public digital cultural service in Fujian Province can provide reference for its sustainable development in this field. Referring to the previous research, this paper took the lead in using the binary logistic regression model to study public demand for digital cultural services in Fujian Province, and selecting universities in Fujian Province as the research object. It provides a significant practical reference basis for the construction of public digital cultural service in Fujian Province, and also has a certain theoretical reference value for research related to public digital cultural service. [Method/Process] Culture is related to people's well-being and the all-round development of people. In order to meet the people's growing need for a better life and protect the people's basic cultural rights and interests, there are more urgent requirements. Public digital cultural service, in the form of digitalization, breaks the restrictions of time and space, and serves as an important magic weapon to get through the "last mile" of public cultural service and improve the accessibility of the service. Digital construction helps to enhance the accessibility of public cultural service, improve the coverage and dissemination efficiency of the service, and promote public cultural participation. On the basis of the relevant literature, the article intends to discuss the impact and mechanism of public digital cultural service, that is, which factors have an impact on public cultural needs? How do these factors affect the public digital cultural service? What is the impact? Under what conditions is the impact more significant? This study was carried out by combining the methods of literature survey, quantitative and qualitative analysis, and obtaining sample data of users through the questionnaire to analyze the statistics with the SPSS 21 software and verify relevant research hypotheses and regression models. [Results/Conclusions] It is found that resource, subject, platform, infrastructure, and service efficiency can effectively strengthen the requirements for the public digital cultural service in Fujian Province. In view of this, we suggest establishing diversified platforms for digital service, integrating cultural resources, enhancing the establishment of service personnel, strengthening promotion and popularization, optimizing the facility and equipment of public culture, and improving the environment of the places. In the analysis of the influencing factors of public digital cultural service, this study is not comprehensive enough. In the future, it is possible to sort out the factors affecting public digital cultural service more comprehensively, and a more comprehensive sample can be used in our study.