The cascading propagation and evolution of metro operation failures can significantly impact the safety of metro operation. To overcome this challenge, this study pre-processes a massive amount of metro operation log data through noise reduction. Moreover, a professional terminology dictionary is constructed along with a custom stop-word dictionary to segment the preprocessed data. Subsequently, the AFP-tree algorithm is employed to mine the segmented log data and identify key hazards. A weighted urban rail transit network is established, considering the effective path time cost, and the shortest travel OD path. To simulate the dynamic evolution of the failure chain propagation, a model based on disaster propagation theory is constructed. Taking the Shanghai Metro line as a case, multiple simulation scenarios are established with 25 key hazards as triggering points, and the number of cascade failure stations affected under different scenarios is outputted. The results indicate that the fault stations caused by the large passenger flow are the largest. Meanwhile, the number of stations affected by the door clamp is the smallest. The scale of fault stations reaches a maximum value in 16–20 min. Through case analysis, a positive correlation is found when the self-recovery factor is between 14 and 18, and the number of fault stations shows a significant increasing trend. The research results can provide decision-making support and theoretical guidance for rail transit operation safety management enterprises.

地铁运营故障的级联传播和演化会严重影响地铁运营的安全。为了克服这一挑战，本研究通过降噪的方式对大量地铁运营日志数据进行预处理。此外，还构建了专业术语词典和自定义停用词词典来对预处理数据进行分段。随后，采用AFP树算法挖掘分段日志数据并识别关键危险。考虑有效路径时间成本和最短出行OD路径，建立加权城市轨道交通网络。为了模拟故障链传播的动态演化，构建了基于灾害传播理论的模型。以上海地铁线路为例，建立多个模拟场景，以25个关键危险源为触发点，输出不同场景下受影响的级联故障站点数量。结果表明，客流过大造成的故障车站最多。同时，受门夹影响的工位数量最少。故障站点规模在16~20 min内达到最大值。通过实例分析发现，自恢复因子在14~18之间呈正相关，且故障站数呈现显着增加趋势。研究成果可为轨道交通运营安全管理企业提供决策支持和理论指导。