Abstract: The safety risk event data of railway passenger stations are mostly stored in text form, which is difficult to efficiently and quickly query. To fully leverage the value of data, this paper investigated the construction and application of a knowledge graph of railway passenger station safety risk events, proposed a knowledge graph construction framework suitable for railway passenger station safety risk event management, studied the knowledge extraction method based on BERT BiLSTM CRF model, and constructed the data layer based on the safety risk event data of a certain passenger station. The experiment showed that the model had better performance than other mainstream recognition technologies. The paper constructed a knowledge graph for railway passenger station safety risk events, implemented structured storage and display of graph data through Neo4j, and designed safety risk event intelligent question answering system based on this knowledge graph, which can provide efficient and intelligent responses to user questions that meet real scenarios and needs, effectively improve the retrieval efficiency of railway passenger station safety risk events.