Abstract: In view of the privacy governance dilemma caused by the sensitivity of leakage data, the diversity of leakage subjects, and the complexity of leakage scenarios in large language model applications, this study aims to construct a multi-dimensional privacy risk classification framework, systematically reveal the generation mechanism and evolution path of privacy risks, and construct a scientific and effective privacy governance strategy. Based on the three-level coding method of grounded theory, this paper analyzes the representative literature and laws and regulations at home and abroad, constructs a large language model privacy risk classification framework, analyzes the risk evolution path through the interaction relationship and data flow of users, platforms, external regulators and other subjects, and explores its internal logic from the perspective of life cycle. The privacy risk classification framework for large language models encompasses five major categories: privacy policy risks, internal governance risks, technical security risks, user behavior risks, and external regulatory risks, along with 33 specific risk factors. It reveals that privacy risks have a transmission mechanism among users, platforms, and regulators, and exhibit dynamic evolution throughout the life cycle stages of data collection, model training, and deployment application. Based on this, a multi-stakeholder collaborative "holistic governance" strategy is proposed. This study breaks through the traditional single-perspective static analysis paradigm of privacy risks and deepens the theoretical understanding of the formation mechanism and evolution path of privacy risks in large language models.