Convolution neural network (CNN) is sensitive to spatial features, while Inception has the advantage of multi-scale feature extraction compared with CNN, long-short-term memory network (LSTM) is sensitive to temporal features, and deep short-term memory network (DLSTM) has deeper feature extraction advantages than LSTM. In order to fully extract the spatial and temporal characteristics of rolling bearing vibration signals in multi-scale, a dual-channel rolling bearing fault diagnosis model Inception-DLSTM based on the combination of Inception channel and DLSTM channel is proposed. For the Inception channel, the time-frequency diagram generated by the wavelet transform of the bearing vibration signal is used as the input, and the multi-scale Inception network is used to extract the spatial feature information of the time-frequency diagram; for the DLSTM channel, the bearing vibration signal is directly taken as the input, and the DLSTM network is used to fully extract the time feature information of the signal.Then the feature information output from the two channels is connected into a spatio-temporal feature vector, and finally the classifier is used to diagnose and identify the bearing fault. Comparing the bearing fault data can be obtained, and the fault identification accuracy of the Inception-DLSTM dual channel can reach 100%, and has good fault diagnosis and feature extraction capabilities.