This paper aims at the problem of internal covariate displacement in the training process of extracting transferable features based on the convolutional neural network (CNN) domain adaptive technology. A multidomain adaptive rolling bearing fault diagnosis method is proposed. First, use CNN to extract the migratable features of the original vibration data; Secondly, multilayer domain adaptation and weight regularization terms are used to constrain CNN parameters to further reduce the distribution difference of migratable features, thereby solving the problem of domain shift; Finally, the rolling bearing data set of Case Western Reserve University was used for experimental verification. The results show that this method can effectively reduce the difference in feature distribution between the source domain and the target domain, and improve the diagnostic performance of the CNN model on the target domain dataset, compared with the adaptive fault diagnosis method at the highest level, the proposed method can achieve higher classification and recognition results in the fault diagnosis of migration between two data sets.