In order to solve the problems of low error-tolerance rate and low diagnosis precision of traditional machine learning algorithm in rolling bearing fault diagnosis of Cross-platform under different working conditions, which a rolling bearing fault diagnosis method based on the fusion of Continuous Wavelet Transform (CWT) and Transfer Learning (TL) was proposed in this paper. In this method, the time-domain signals of rolling bearing fault signals under Cross-platform and different working conditions were extracted as source domain samples and target domain samples respectively, and the vibration signals were transformed into two-dimensional signals by CWT algorithm. Then, the fault signals were mapped to the Reproducing kernel Hilbert Space through Kernel function, and the loss function of the Convolutional Neural Network (CNN) was optimized to reduce the distribution difference between the source domain and target domain samples after transfer learning using the Multi-Kernel Maximum Mean Discrepancy (MK-MMD) distance as the metric. Finally, CNN model was used for the pattern recognition of the matched source domain and target domain samples to realize fault transfer diagnosis of Cross-platform rolling bearings under different working conditions. Experimental results show that compared with other methods, the proposed method improves the accuracy and robustness of fault diagnosis of rolling bearings significantly under Cross-platform and different working conditions.