There is a large amount of failure information from the engine after-sales maintenance and failure reports. This study introduces knowledge graphs and designs a systematic building procedure for the field of engine fault. It carries out ontology modeling for the multi-source fault data. The entity recognition framework that combines BERT with BiLSTM-CRF is used to mine expert knowledge in fault data. The index FF-IEF (fault frequency-inverse event frequency) is proposed, and fault diagnosis is performed based on the knowledge graph and Bayesian network. We design and develop the prototype system EFKG that contains 12534 entities and 408972 triplets. The system provides knowledge extraction, visual retrieval, and auxiliary decision-making. It can effectively improve the efficiency of information retrieval and maintenance and is of guiding significance for the application of knowledge graphs in the field of engine fault.