With the increase in grid size and structural complexity, as well as the widespread integration of power electronic devices and distributed energy sources, power systems face new challenges in fault identification and analysis. This study explores how artificial intelligence techniques can be utilized to address these challenges. In particular, we focus on the application of deep learning in big data environments, whose powerful capabilities in automatic feature learning, nonlinear model fitting, and end-to-end modeling provide new solutions for high-complexity grid fault identification. The study provides an in-depth analysis of the current state of fault identification and analysis in power systems and proposes innovative fault detection and localization methods by combining artificial intelligence techniques, especially deep learning. Through case studies, experimental results show that this method can effectively improve the accuracy and efficiency of fault identification while reducing the dependence on human intervention. The experimental results verify the practicality and effectiveness of the proposed method in complex grid environments. In summary, this study provides new perspectives and tools for grid fault identification using artificial intelligence techniques, especially deep learning, which is of great theoretical and practical significance for enhancing the safe and stable operation of power systems. Future research directions will focus on further optimizing the performance of the algorithm, improving the ability to handle real-time large-scale data, and adapting to the dynamically changing fault analysis needs of the power system.