Abstract: ［Objective］ As the urban rail transit operation and maintenance system is large and complex, it is somewhat difficult to accurately analyze the operation and maintenance risks using traditional data. Incorporating big data analysis into risk management is a development trend. By constructing an event evolutionary graph for urban rail train operation and maintenance safety, new perspectives and tools are provided for urban rail transit safety management, risk prevention and control, thereby providing a theoretical basis and methodological support for improving the safety of urban rail transit systems. ［Method］ Focusing on the construction of event evolutionary graph for urban rail train operation and maintenance safety, three key steps for constructing the event evolutionary graph are optimized. In the event extraction stage, a professional dictionary of urban rail is constructed and trigger word rules are designed; in the event relationship extraction stage, semantic dependency and sequential relationship are combined; in the event evolutionary alignment stage, an optimized clustering algorithm is used to define the main risk factors. Through comparative evaluation of traditional knowledge graph methods, general event evolutionary graph methods and the above proposed method, accuracy and effectiveness of the last one in the construction of event evolutionary graph are verified. Finally, the data storage and visualization of the event evolutionary graph are realized through Gephi software. ［Result & Conclusion］ Traditional fault analysis based on mathematical statistics and risk analysis relying on manual experience have certain limitations in terms of predictability, comprehensiveness, and timeliness. The complete path from theory to application of event evolutionary graph in operation and maintenance risk management is presented, and a comprehensive methodology for data analysis and visual representation is realized. With the continuous improvement of methodology and the deepening of empirical research, it is believed that this paradigm for extracting the theoretical graph of urban rail transit field will further promote the improvement of urban rail transit safety management level.