Aiming at the difference of bearing fault data feature distribution caused by complex working conditions in industrial scenes and the difficulty of obtaining a large number of labeled data, an one-dimensional convolution subdomain adaptive adversarial neural network (SANN) based on Wasserstein distance and local maximum mean discrepancy ( LMMD) is proposed. Firstly, the network constructs a feature extractor based on CNN for pre-training and learning the domain feature representation. In the adversarial training stage, the adversarial layer introduces Wasserstein distance to measure the difference between the source domain and the target domain, realize the alignment of marginal distribution and solidify the training results. In the feature extraction layer, LMMD calculation module is introduced to capture the fine-grained information of each category to realize the alignment of conditional distribution. The performance of the model is verified by the bearing fault data sets under two different working conditions. The experimental results show that under unsupervised conditions, the proposed method improves the recognition accuracy by 5. 0% and 6. 9% respectively compared with the basic domain adversarial network on the target data set, and the performance is better than the existing migration algorithms.