Abstract: Abstract： In view of the problems of large workload， low efficiency and high cost of manual extraction of aircraft fault information， a method of information extraction based on domain dictionary， rules and BiGRU-CRF model is proposed. Combining the characteristics of aircraft domain knowledge， domain dictionary and template rules are constructed based on aircraft fault text information， and semantic labeling of fault information is carried out. The BiGRU-CRF deep learning model is used for named entity recognition. BiGRU obtaines the semantic relationship of context， and CRF decodes and generates the entity label sequence. The experimental results show that the information extraction method based on domain dictionary， rules and BiGRU-CRF model has an accuracy of 95.2%， which verifies the effectiveness of the method. It can accurately identify the key words in the aircraft fault text， such as time， aircraft type， fault part name， fault part manufacturer and other information. At the same time， according to the domain dictionary and rules to correct the recognition results， effectively improves the efficiency and accuracy of information extraction， and solves the problem of traditional entity extraction model long-term dependence on manual features.

Key words: Key words： fault information, information extraction, named entity recognition, BiGRU-CRF, domain dictionary