Abstract: In order to address the complexity, magnanimity, and multi-level characteristics of high-speed train components and parts knowledge, and improve the effectiveness of intelligent Q&A for high-speed train components and parts knowledge graph, this paper proposed a knowledge subgraph matching model of high-speed train components and parts knowledge graph based on situational awareness model and classification model. This model extracted scene features and transformed vectors through situational model, fused the word vector and situational vector, input them into the Bidirectional Encoder Representation from Transformers (BERT) model, and classified the knowledge domain to which the user question belongs. The classification results were the results of knowledge subgraph matching. Experimental results show that the proposed model has better performance indicators in all aspects compared with other mainstream classification models.