A semi-supervised transfer learning model WSTLPL (weakly supervised transfer learning based on pseudo-label) was proposed to solve the problem of poor generalization ability of transfer model due to the lack of target domain label data. The convolutional neural network was used to learn the transferable characteristics of the original vibration data, and the network was pre-trained with the source domain data;the trained network was used to predict the target domain data category, and the class label with the highest classification probability was taken as the pseudo-label of data. According to the regularization term of domain adaptation and pseudo-label learning, the parameters of neural network were constrained to reduce the distribution differences of the learned transferable features. The results show that the proposed transfer model is more accurate than the existing diagnostic model.