Aiming at the requirements of intelligent maintenance and digital diagnosis of commercial aircraft in China, a novel Boyer-Moore long short-term memory network (BM LSTM) algorithm was proposed for unstructured fault isolation manual. A majority voting method was used to fuse three entity recognition algorithms including conditional random fields (CRF), bi-directional long short-term memory (BiLSTM) and BiLSTM CRF. The accuracy of entity recognition was effectively improved by the proposed BM LSTM algorithm. On this basis, a maintenance scheme knowledge graph was constructed for the commercial aircraft maintenance fault diagnosis manual. A commercial aircraft maintenance scheme recommendation system was designed by combining term frequency-inverse document frequency (TF-IDF) similarity algorithm with BM LSTM. Maintenance schemes can be matched accurately with this recommendation system by retrieving the unstructured fault description texts. Experimental results show that the proposed knowledge graph and the maintenance scheme recommendation system can effectively ensure the accurate matching of maintenance information, and the efficiency of maintenance scheme formation is significantly improved.