Automatic Subject Indexing (ASI) plays a critical role in knowledge organization and information retrieval. However, existing studies remain fragmented, often emphasizing algorithmic details while overlooking the broader intellectual structure and thematic evolution of the field. To address this gap, this study constructs a comprehensive knowledge map of ASI research spanning 2000–2024 by integrating scientometric assessment with content analysis. Based on 1,161 publications retrieved from Web of Science and Scopus, we applied co-authorship, co-citation, and thematic evolution analyses to reveal influential contributors, collaboration patterns, and topic dynamics. To complement the scientometric insights, 26 representative studies on technical innovations and domain applications were examined in depth. Results reveal three major developmental phases: an initial reliance on rule-based approaches, a subsequent shift toward statistical and machine learning techniques, and the current dominance of deep learning architectures. Within this latest phase, large language models (LLMs) have emerged as a transformative development, while recent research also underscores multilingual indexing, ontology alignment, and linked data integration as critical directions for improving semantic interoperability, knowledge organization, and retrieval effectiveness. Despite progress, persistent challenges include data scarcity for low-resource languages, limited alignment between algorithmic outputs and professional cataloging standards, and the opacity of deep learning models. Key opportunities include leveraging LLMs for context-aware indexing, advancing human-in-the-loop workflows, and promoting open datasets and benchmarks to foster transparency and interoperability. This study offers an integrated analysis of ASI research, providing structural insights, critical interpretation of methodological trends, and forward-looking perspectives for scholars and practitioners.
