Driven by the dual forces of digital transformation and intelligent education, university libraries are transitioning from traditional document storage centers to smart service hubs. This paper focuses on data-driven theory and AI technology applications, integrating the characteristics of university library service scenarios to construct a closed-loop smart service model of "data collection-intelligent analysis-precise service-feedback optimization." By analyzing current pain points in university libraries regarding resource integration, service responsiveness, and user demand matching, it explores three dimensions: intelligent resource restructuring, precise service delivery, and smart management upgrades. Specific implementation pathways are proposed by integrating big data analytics, machine learning, and natural language processing technologies. The research provides theoretical references and practical insights for university libraries to overcome service bottlenecks, enhance user experience, and strengthen educational support functions.
Keywords: Data driven, AI empowered, university libraries, intelligent services, service models