Artificial intelligence (AI) is transforming library information services by enhancing user interaction, resource management, and academic support. However, fine-grained topic identification and evolution analysis in this field remain underexplored. This study identifies key research topics and tracks their development to uncover AI’s trajectory in library information services. Leveraging deep learning-based topic modeling techniques—BERTopic and Dynamic Topic Modeling (DTM)—we analyzed 2,789 filtered article abstracts retrieved from the Web of Science Core Collection and Scopus databases published between 2015 and 2024. The modeling process involved several steps: we first generated document embeddings using Sentence-BERT, followed by dimensionality reduction with UMAP to project the high-dimensional vectors into a lower-dimensional space. We then applied HDBSCAN, a density-based clustering algorithm, to group semantically similar documents. Topic representations were constructed using class-based TF-IDF (c-TF-IDF) to identify the most representative keywords for each cluster. Finally, we employed DTM to examine the temporal evolution of these topics over the 2015–2024 period. The study identified 27 topics, including OCR for historical documents, multilingual processing, book recommendation systems, author name disambiguation, and misinformation detection, among others. These were further categorized into ten research directions, such as digital transformation, information retrieval, and scholarly communication. Temporal analysis revealed emerging AI-driven trends alongside steady growth in traditional areas like information literacy education. Findings highlight the growing integration of AI technologies into library information services. This study offers strategic insights for researchers and practitioners to innovate library information services and responsibly adopt AI technologies.
Xu et al. (Fri,) studied this question.