Abstract: In order to improve the accuracy of bearing fault diagnosis under the condition of unbalanced samples, a sample expansion method based on VAE-SNN and a fault diagnosis method based on unbalanced loss network are proposed. Firstly, the variational self-coder is used to generate data, and the category of generated data is determined according to the twin neural network. The sample expansion based on the variational self-coder and the twin neural network is realized; Secondly, the disadvantages of convolutional neural network are analyzed. Aiming at the particularity of unbalanced samples, an unbalance loss function convolutional network is proposed, which can automatically focus on the training of small and difficult samples. The experimental results show that the similarity of the expanded samples of the generated countermeasure network is 0.847, and the similarity of the expanded samples of the twin neural network is 6.61% higher than that of the countermeasure network, indicating that the sample expansion effect of the twin neural network is better; On the premise of the same diagnosis method, the accuracy rate of the expanded sample is increased by 9.42% compared with that before the expansion, which indicates that the expanded sample is useful to improve the accuracy rate of bearing fault diagnosis; The diagnosis accuracy of unbalanced loss network is 7.17% higher than that of convolutional neural network and 4.12% higher than that of adaptive deep learning, which verifies the high accuracy and superiority of unbalanced loss network.