In response to the problems of strong professionalism， low value density， scattered domain knowledge， and lack of effective integration and utilization methods in the fault data of key components of aviation equipment， driven by the demand for intelligent fault diagnosis， a knowledge graph was introduced to organize the knowledge contained in fault records for sharing and reuse， and the construction and application of fault knowledge graph were studied. Firstly， based on the analysis of prior fault knowledge and fault records， a hierarchical fault diagnosis knowledge ontology model for key components of aviation equipment was designed， which defined entity types and their relationship constraints， effectively avoiding unclear entity boundaries and laying the foundation for the structured representation of knowledge. Secondly， an improved knowledge extraction method based on set prediction， namely SPN-BiLSTM-CRF， was proposed to efficiently extract knowledge triple sets directly from unstructured Chinese fault records， and a knowledge graph of hydraulic piston pump faults was constructed using aircraft component hydraulic piston pump as an example. Finally， combined with the FP-Growth association rule mining algorithm， association rules among fault modes， fault causes， and fault states were extracted from the fault knowledge dataset， and fault diagnosis was realized on this basis. SPN-BiLSTM-CRF can effectively address the knowledge application problem in fault data and provide a knowledge-driven solution for intelligent operation and maintenance of aviation equipment.