Abstract: In response to issues such as low efficiency in asset management and insufficient decision-making support in universities, this article explores the application strategies of Artificial Intelligence Generated Content (AIGC) technology in intelligent asset management. By analyzing core AIGC technologies such as Generative Adversarial Networks (GAN), Natural Language Processing (NLP), and multimodal learning, and considering the characteristics of university asset management, it proposes adaptation solutions for scenarios like intelligent asset inventory, predictive maintenance, and resource allocation optimization. The article analyzes implementation difficulties such as unstructured data processing, model generalization ability, and data privacy, and puts forward countermeasures such as constructing a “human-AI collaboration” management model and formulating data standards and sharing mechanisms. It also presents two major innovations: Firstly, technological integration innovation, which for the first time proposes a three - dimensional asset management model for universities that combines AIGC with digital twin technology. Secondly, management model innovation, which designs a transformation path from the “passive response” paradigm to the “active prediction” paradigm. Research shows that AIGC technology can realize intelligent asset inventory and accurately predict asset requirements. It is of great significance in reducing labor costs, optimizing resource allocation, and providing a scientific basis for decision-making. It can effectively promote the intelligent and modern process of university asset management and significantly improve the management level.