面对“双高”运行态势，传统模型驱动的电力系统稳定分析方法逐渐暴露出效率与适应性不足的局限。人工智能技术凭借其在速度与精度方面的优势，正成为该领域的重要研究方向。因此，聚焦电力系统稳定分析技术从模型驱动到数据驱动，再到知识-数据融合驱动形成混合智能的发展路径。首先梳理了典型稳定分析任务的技术需求，并回顾了传统模型驱动方法；随后总结了人工智能方法在不同场景中的应用成果，分析其优势与不足；进一步探讨了融合电力系统知识与机理的信息驱动方法及其研究进展；最后，结合“双高”背景下的电力系统特征，分析了当前面临的挑战，并展望未来研究方向。

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.