关键词: 高校图书馆, 资源建设, 效能评估, 人工智能

Abstract:

[Purpose/Significance] Artificial intelligence is reshaping the landscape of higher education, driving the transformation of university library resource development from a "resource supply-oriented" model to an "efficiency-driven" one. Traditional evaluation systems, constrained by single-dimensional indicators and manual data collection, fail to meet the demands of intelligent transformation. This study takes the Library of Beijing Institute of Technology (BIT), a research university library, as a case study to construct a three-dimensional efficiency evaluation system for resource development. Unlike previous studies that focus on theoretical model construction, this research integrates the practical experience of the BIT Library since 2021, combining team building, technological empowerment and institutional design to form an implementable evaluation system, which provides replicable references for the intelligent development of resource construction in domestic university libraries. [Method/Process] This study adopts a mixed research method combining case study and quantitative-qualitative analysis, with theoretical foundations in library science theories such as resource life cycle management and interdisciplinary theories including artificial intelligence application. The empirical data are derived from the operational data of the BIT Library from 2021 to 2025 (such as procurement records and user behavior data). The construction of the evaluation system consists of five links: 1) We establish a hierarchical training system covering all librarians, offering expert lectures and implementing the pairing model of "data analyst + subject librarian"; 2) We use the analytic hierarchy process (AHP) to determine indicator weights, adding the "interdisciplinary adaptability" indicator for emerging fields, and building a full-process evaluation model; 3) We construct an AI-empowered platform integrating multi-source data, which significantly shortens the duration of manual data processing; 4) We carry out in-depth research in collaboration with colleges and research teams, and conduct benchmarking analysis with top university libraries; 5) We establish a scientific decision-making mechanism linking evaluation data with the University Library Committee and various colleges of the university. [Results/Conclusions] The application of this system in the BIT Library has achieved remarkable results: the accuracy of library resource guarantee has been significantly improved, the efficiency of resource utilization has risen substantially, the capacity for scientific research support has been notably enhanced, and the service satisfaction has undergone a leapfrog improvement. However, the evaluation system still has limitations such as data privacy risks, insufficient AI literacy of some librarians, and lack of inter-library collaboration. Suggestions for targeted action include adopting federated learning technology to protect data privacy, carrying out hierarchical AI training, and establishing a regional evaluation alliance. Future research will explore the specific application of generative artificial intelligence in the evaluation system and establish a dynamic adjustment mechanism for indicators adapted to technological and disciplinary development.

Key words: university library, resource construction, efficiency evaluation, artificial intelligence