Aiming at the problem of poor generalization ability of fault diagnosis of traditional deep learning network model under variable working conditions, a fault identification method based on the fusion of transfer learning bidirectional long short memory network and attention mechanism ( TLBA) is proposed. Divide the original fault data into source domain and target domain, and construct a bidirectional long short-term memory network (BA) model that integrates attention mechanisms, and then use this model to learn source domain data features. Finally, transfer learning is used to further optimize and adjust the network parameters of the BA model by learning the data in the target domain, and finally the fault classification identification model in the target domain is obtained. Taking the aircraft wing beam fault as an example, the results show that compared with the traditional fault diagnosis method BiLSTM-Attention, the comprehensive evaluation index F1-score of this method is improved by 3. 4%, and the average fault diagnosis accuracy is above 91%. At the same time, the fault classification results under variable operating conditions are relatively stable.