The electric submersible pump well system is an important tool for oilfield exploitation owing to its advantages of large displacement, high head, and flexible operating environment. Reducing the hazards of the faults in the electric submersible pump well system requires the fault components to be quickly and precisely located and repaired. This study proposes a knowledge graph-based fault diagnosis method for electric submersible pump wells. The improved bi-directional long short-term memory-conditional random field (BiLSTM-CRF) entity identification algorithm and the bidirectional encoder representations from transformers (BERT) relation extraction algorithm are used to extract expert knowledge from fault data and then construct a knowledge graph in the field of fault diagnosis of electric submersible pump wells; a Bayesian inference network with fault signs as initial nodes is built with the constructed knowledge graph, and the cause of the fault is inferred by utilizing historical fault data and the calculation method of decoupling conditional probabilities. The proposed method is validated by real fault diagnosis cases.