Abstract: [Purpose/Significance] This study explores the application of artificial intelligence technology in the field of library cataloging, with the aim of optimizing cataloging processes, improving cataloging efficiency and enhancing cataloging quality. [Design/Methodology] Building upon a review of relevant domestic and international research and practice, this study constructs a multi-dimensional framework for the application of AI in library cataloging tasks—covering content, standards, technology, processes and tools—and provides a detailed analysis of the current state of practice and future development trends within each application scenario. [Findings/Conclusion] In bibliographic record generation, selecting an appropriate cataloging model can automate the creation of metadata and optimize cataloging workflows; in metadata management, named entity recognition, metadata enrichment and quality control are key technologies and approaches; in automated indexing, the integrated application of open-source tools and generative AI will effectively enhance the performance of traditional indexing tools. It sets out the fundamental requirements for advancing its application: adhering to cataloging standards and specifications; promoting the integration of linked data and AI; facilitating the transformation and upgrading of library system platforms; and relying on the expert guidance of catalogers. [Originality/Value] Drawing on research findings in the field of library cataloging datafication, this study identifies typical AI application scenarios in cataloging work, clarifies the key areas and considerations of implementation, and provides guidance for the targeted development of AI-assisted cataloging in China, as well as serving as a reference for the macro-level planning of library cataloging development.

Keywords: Library cataloging, Metadata enrichment, Metadata generation, Artificial Intelligence, Automated indexing