With the promotion of the major strategy of national transportation power, the super-large-scale metro operation network has become an inevitable trend, and operation safety has become increasingly prominent. Metro operation dispatch logs and accident reports were taken as the research object, the hazard sources were efficiently and accurately identified, the risk chains of hazard sources were mined, and the risk evolution mechanism was revealed. Firstly, the transportation lexicon was constructed to improve the accuracy of word segmentation, the text features were extracted based on the term frequency-inverse document frequency (TF-IDF) algorithm, and the key eigenvalues were mined to identify the key hazard sources. Secondly, pattern matching was used to extract explicit causality, and the Self-Attention BiLSTM extracted implicit causality and integrated event trigger word position identification to enhance the effectiveness of implicit causality extraction. Finally, an example was given to verify the efficiency and accuracy of the model, the experiments showed that the F value of extraction effect was increased by nearly 10%, the extraction accuracy was 87.53%, the identification of operational hazard sources was more accurate, and the visualization of risk evolution law was realized.

随着国家交通强国重大战略的推进，超大规模地铁运营网络成为必然趋势，运营安全问题日益凸显。以地铁运营调度日志和事故报告为研究对象，高效准确识别危险源，挖掘危险源风险链，揭示风险演化机制。首先构建交通词典提高分词准确率，基于词频-逆文档频率（TF-IDF）算法提取文本特征，挖掘关键特征值识别关键危险源。其次，模式匹配用于提取显式因果关系，Self-Attention BiLSTM 提取隐式因果关系并集成事件触发词位置识别，以增强隐式因果关系提取的有效性。最后给出实例验证了模型的有效性和准确性，实验表明提取效果F值提升近10%，提取准确率为87.53%，操作危险源识别更加准确，实现了风险演化规律的可视化。