Artificial intelligence (AI) is reshaping scientific research paradigms at an unprecedented speed and breadth.From protein structure prediction to assisting drug development, AI technology has become an important tool in scientific research activities. However, current AI still has significant limitations in reasoning ability, knowledge generation, and experimental design, and cannot fully replace the role of human scientists. As a part of the knowledge infrastructure, the library must reposition its function and value in the scientific research ecosystem. Based on the current development trend of AI scientific applications, this article analyzes its impact and challenges on libraries, and proposes five key action strategies for the future to achieve the transformation from an “information service provider” to a “research collaborator”.

By conducting a systematic study of China’s artificial intelligence governance policies through thematic modeling and quantitative evaluation, this paper provides support and guidance for policy optimization. Effective data from the 92 nationallevel artificial intelligence governance policies issued in China from 2017 to the 15th of March, 2025 were preprocessed.The BERTopic model was used to identify policy themes, calculate keyword c-TF-IDF weights, and three other thematic indicators, and combined with policy content to conduct an in-depth analysis of governance policy priorities. Based on thematic modeling, the PMC policy evaluation model was constructed to conduct empirical quantitative analysis on nine representative policy documents. The BERTopic model identified 13 themes, covering nine key areas of China’s AI governance policies. The PMC index model indicates that the overall policy level is good, but further optimization is needed in terms of governance framework, governance tools, and governance entities. Based on this, policy recommendations were proposed, including the construction of a classified governance policy framework, the combination of policy tools, and the promotion of collaborative governance, to serve China’s AI governance practices.

In the context of national strategies for educational empowerment and digital China initiatives, the development of smart libraries has become a pivotal component of the national cultural digitalization strategy, necessitating artificial intelligence (AI) to drive service model innovation. This paper focuses on university library circulation and reference service scenarios, guided by the practical needs of faculty and student users. A general-purpose agent-based intelligent question-answering system was developed using a knowledge base of frequently asked questions (FAQs) in circulation operations. By integrating AI large language model to achieve automated knowledge reasoning and response generation, the system significantly enhances service efficiency while optimizing limited human resources. Empirical results demonstrate that this solution markedly improves library service effectiveness, provides a reusable technical framework for intelligent transformation in other domains, and contributes to advancing digital transformation and modernization in higher education.

The proliferation of Artificial Intelligence Generated Content (AIGC) is fundamentally challenging the ontological foundations and managerial logic of Information Resource Management (IRM). Traditional IRM paradigms, predicated on a static lifecycle, prove inadequate for the extreme dynamism, emergent collaboration, and heterogeneous complexity of the AI open-source ecosystem. This paper, through an analysis of communities like Hugging Face and domestic models such as DeepSeek and Qwen, identifies three critical dimensions of generative information resources: Throughput (T),Quality (Q), and Capability Ceiling (C). Integrating insights from established theories, this paper constructs the dynamic Information Resource Management Theory (IRMT), a novel framework featuring the T-Q-C trade-off triangle to guide the strategic optimization of AI resources. This work offers more than a new theoretical paradigm for an autonomous IRM knowledge system in the AIGC era; it also delineates a practical path toward achieving efficient application of domestic AI and technological self-reliance, especially under computational constraints. Future research avenues include the quantitative modeling of the T-Q-C framework, the integration of ethical governance, and the extension of the theory to multimodal and agent-based systems.

To understand the formation of academic figures’ relationships in the Song Dynasty and explore the integration path between digital humanities technology and traditional historical research, a Song Dynasty academic network involving 1 987 scholars and 2 742 groups of relationships was constructed based on the china biographical database(CBDB). The research focused on three core issues: the impact of scholars’ attributes (hometown, school identity,etc.) , social capital (centrality, structural holes, etc.) , and the endogenous structure of the network (reciprocity,transitivity, etc.) on the establishment of academic relationships. Geographic proximity and homogeneity of school identity have a significant positive effect on the formation of academic connections. The network shows strong reciprocity but suppressed transitivity, reflecting the bidirectionality of academic interactions and the competitive exclusion between schools. In terms of social capital, centrality, especially degree centrality and eigenvector centrality,has a significant impact on relationship building, while the role of structural holes is limited. This study provides empirical evidence for understanding the social foundation of academic dissemination in the Song Dynasty and offers a new path for the integration of digital humanities and traditional historiography.

This paper focuses on the construction of a knowledge graph for the chronological resources of Xian Xinghai.Based on Qin Qiming’ Brief Chronicle of Xian Xinghai (1905—1945), an ontological knowledge model was developed,encompassing core entities such as persons, time, locations, events, works, and documentary sources. The Neo4j graph database was employed for data storage and relational querying, while the Vis.js library facilitated the dynamic visualization of Xian Xinghai’s social network and life trajectory. The results demonstrated that knowledge graph technology can effectively enhance the analyzability and visualization of Xian Xinghai’s life events, social networks, and artistic creation context, overcoming the limitations of traditional linear chronological representations and providing new research methods and pathways for understanding his music and revolutionary practices across different regions and stages. Future work may focus on enhancing the efficiency of knowledge extraction, expand data sources and enhance the interactive visualization experience.

In the “screen-reading era”, user demand for knowledge content in media formats such as video, audio, and images has surged dramatically. During the 15th Five-Year Plan period, libraries are undergoing a critical transition from intelligent to smart operations. Within the macro framework of building high-quality data resources, establishing a comprehensive media knowledge organization system, efficiently managing media resources, and unlocking their new value to provide users with diverse knowledge access and service support has become particularly crucial. This paper examines the case study of Guangzhou Library’s smart media resource management and service platform development. Addressing current challenges in library media resource management and application, it summarizes experiences and outcomes in resolving these pain points, and proposes further concepts for the 15th Five-Year Plan period. Further envisions how to deepen and refine media asset intelligent applications from three dimensions—“enhancing resource utilization, enhance AI capabilities, and building a humanistic hub”—to unlock the platform’s potential value in public cultural services and academic research support. This aims to provide new pathways for consideration in the construction and application of high-quality data resources for libraries.

This study targets the national security intelligence analysis scenario, aiming to overcome the informational limitations of single-modal data analysis and enhance situational awareness in complex scenarios. Based on deep learning theory, this paper constructs a heterogeneous data representation model from the macro perspective of multimodal data fusion, organizes a cross-modal data fusion framework, and introduces a dynamic weight allocation mechanism to achieve effective integration of multi-source heterogeneous data such as social media text, video, audio, satellite imagery, and network traffic. The research indicates that, in a complex data ecosystem, attention should be paid not only to the granularity of multimodal data fusion in open-source intelligence, but also to the requirements of intelligence tasks, enabling rapid and accurate fusion to form a practical and feasible industrialized process.

In the context of the marketisation of data elements, revealing the integration mechanism between scientific data and the various elements of new quality productive forces can provide theoretical references and practical guidance for promoting the value transformation of scientific data and accelerating the development of new quality productive forces. Following the “path dimension-implementation path” framework, this paper analyses the path dimension of scientific data empowering the formation of new quality productive Forces from three horizontal dimensions: technology,institutions, and ecology. It then proposed specific implementation paths for scientific data to empower new quality productive forces from three vertical dimensions: new-type workers, new-type means of production, and new-type objects of labour. Scientific data empowers the formation of new quality productive forces through three pathways: driving innovation in research paradigms, facilitating the market-based allocation of data elements, and constructing cross-subject collaborative innovation networks. This can be achieved by enhancing the data literacy of new-quality workers, implementing legal governance of scientific data, and endowing traditional labour objects with new characteristics to empower new-quality workers, new-quality labour materials, and new-quality labour objects through scientific data.