With the increasing complexity of "high-proportion renewable and high-electrification" power systems, traditional model-driven stability analysis methods face growing limitations. Artificial intelligence （AI） techniques, offering advantages in both speed and accuracy, have emerged as a key research focus. This paper focuses on the development path of power system stability analysis technology from model driven to data-driven, and further to hybrid intelligence integrating domain knowledge and data. Firstly, the technical requirements of typical stability analysis tasks are outlined, and traditional model driven methods are reviewed.Subsequently, the application results of artificial intelligence methods in different scenarios are summarized, and their advantages and disadvantages are analyzed. Further the information driven methods and research progress of integrating power system knowledge and mechanisms are explored. Finally, based on the power system characteristics under the background of "double high", the current challenges faced are analyzed, and future research directions are discussed.