In order to solve the problem that single sensor signal is easy to be interfered and the degradation information that can be extracted is limited,which leads to low accuracy of bearing residual life prediction,a bearing residual life prediction method based on dual channel information fusion and gated recurrent unit (GRU) neural network was proposed.In the bearing life test,acoustic emission sensor was added as a supplement on the basis of using vibration sensor to collect signals,which could make up for the disadvantage that single signal was easily interfered.After that,the convolutional neural network was used to automatically mine the features containing bearing degradation information,which could avoid the disadvantage of traditional algorithms that rely too much on expert judgment.Then the information was fused through normalization.Finally,these data were used to train the GRU neural network,then the trained GRU was used to predict the remaining life of high-speed railway traction motor bearings.The results show that the neural network model trained with dual channel data are more accurate;the GRU neural network has higher bearing life prediction accuracy than the long and short-term memory neural network.