[Purpose/Significance] Under the backdrop of the accelerated development of AI for Science (AI4S) and national strategies on “AI + Science and Technology”, high-quality, computable corpus resources have become a critical factor for large-model pretraining and intelligent scientific discovery. As the social infrastructures, libraries are facing growing demands to transform toward the construction and provision of AI-oriented corpus resources and services. The article aims to clarify the annotation of the “AI corpus library” and to provide a reference framework for related theoretical research and practical implementation. [Method/Process] By employing conceptual and evolutionary analysis, the article clarifies the essential characteristics of the AI corpus library, interpreting it as a deep integration and functional reconstruction of “Database × Corpus × Digital Library”. It analyzes functional requirements from three dimensions: technological evolution, social application, and regulatory governance. Furthermore, by synthesizing the “text-as-data” model of HathiTrust and the “digital scholarship” practice of the British Library, the paper systematically summarizes the evolutionary logic of the transition from digital libraries to AI corpus libraries. [Result/Conclusion] The study shows that the AI corpus library is a new type of knowledge infrastructure oriented toward “human-machine agent” entities, with multi-modal and computable corpora as its core organizational objects. Its construction should follow an architectural system characterized by data-driven top-level logic, knowledge organization as the intermediate mechanism, and agent-based applications as the functional innovation. The corpusization of existing library collections, the establishment of “non-consumptive use” governance mechanisms, and the embedding of corpus services into digital scholarship workflows represent effective paths for libraries to achieve functional expansion and intelligent upgrading.
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