To solve the problem that the traditional convolutional neural network (CNN) is not high in fault identification accuracy for spiral bevel gear box, an intelligent fault diagnosis method based on deep separation convolutional neural network (DCNN) and Bi-LSTM was proposed. Firstly, the original signal is denoised by wavelet threshold, and then decomposed by empirical mode decomposition (EMD) algorithm. Then, each component of the decomposed eigenmode function (IMF) is kurtosis calculated, and the IMF component with the highest kurtosis value is selected to construct a new vibration signal input model for training. After that, a large number of signal samples are obtained by overlapping the vibration signals, and the spatial feature information of these samples is adaptively extracted from the one-dimensional original vibration signals through the deep separation convolutional neural network. The extracted features are further input into the bidirectional long short-term memory network, and the forward and inverse time-domain vibration signals are extracted at the same time to better extract fault features. At the same time, the residual network is added to the deep separation convolution to reuse the data features, and the convolutional kernel is deeply separated to solve the network degradation problem of the deep model. Finally, the feature information is input into the trained DCNN-Bi-LSTM model to diagnose and identify the spiral bevel gear box fault. The results show that this method can accurately identify gearbox faults, and the highest diagnostic accuracy can reach 100%. Moreover, the proposed method has higher accuracy, stronger anti-noise ability, faster convergence rate and more stable diagnosis results than traditional convolutional neural networks.