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The goals and functions of universities are constantly evolving, yet talent cultivation as the core task of universities has remained unchanged. University libraries play an important role in this endeavor by offering robust academic resource support, promoting knowledge dissemination and innovation, and improving students' independent learning and research capacities. The advent of artificial intelligence, particularly Large Language Model (LLM) technology, offers the potential to refine and restructure academic resources based on personalized needs. This innovation can go beyond the traditional paradigm of academic resource use, make learning materials more diverse, change the way resources are presented, and improve the efficiency of library resource utilization and student learning outcomes. This paper analyzes the shortcomings of contemporary university libraries in supporting student learning. including limited course-related resources, lack of initiative in library learning support services, inaccurate student learning profiles affecting the accuracy of personalized resource recommendations, imperfect resource organization models that cannot meet students' learning needs, and lack of effective cognitive learning theory integration in resource organization. In response to the above problems, a personalized learning support service system based on artificial intelligence technology is proposed. This system is predicated on students' learning and growth, synergizes with the vast academic resources of libraries, and aligns with the evolution and application trajectories of new generation information technologies such as artificial intelligence. It is anchored in the mission of university libraries to underpin talent cultivation, places students' learning at the core, and emphasizes effectiveness of learning and development as the driving force. This system is based on large language model technology, structured within a "knowledge ability literacy" framework, supported by cognitive learning theory, and guided by cognitive hierarchy models. Its goal is to facilitate personalized, autonomous, and efficient learning. The system utilizes self- constructed learning resources, library academic literature, reliable internet sources, and resources generated by large language models to form a diverse and multimodal academic resource base. By considering the needs of individual courses, students, teachers, majors, and topics as entry points, the system broadens the scope and depth of learning services. It also enhances the construction of learning support connotations, making learning support services more precise, efficient, and personalized. This personalized learning support service system is expected to change the library's service model, the organization model and use model of academic resources, thereby supporting the evolution of students' learning methods. [ABSTRACT FROM AUTHOR]

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