Aiming at the problems of S700K switch machine fault diagnosis, which is difficult to extract effective features and signal processing and classification algorithms, a fault diagnosis method for switch machine combining one-dimensional convolutional neural network (1DCNN) and bidirectional long short-term memory neural network (BiLSTM) is proposed. Firstly, the power curve of the switch machine collected by the microcomputer monitoring system is processed. Secondly, the fault features are extracted adaptively from the processed data by the convolution layer and pool layer of CNN. Then through Flatten, the extracted fault features are taken as the input of BiLSTM layer to further mine the deep-level features. Finally, the Softmax function is used to implement intelligent fault diagnosis. The model is validated by the real data provided by a railway bureau. The results show that the accuracy, recall and F1 value of the proposed model reach 98. 99%, 98. 89% and 98. 89% respectively, which are better than other classical fault diagnosis models, 1DCNN-BiLSTM model improves the accuracy of fault diagnosis by at least 1. 08% when the training speed is fast