Abstract: Aiming at the problems of significant differences in cross-domain data distribution, scarcity of labeled data, and neglect of subdomain boundary information by traditional domain adaptive methods in industrial bearing fault diagnosis, this study proposes an unsupervised bearing fault diagnosis method based on categorical disparity-adversarial adaptive networks. The method innovatively integrates the subdomain boundary refinement alignment mechanism, and significantly improves the cross-domain feature consistency by combining the hybrid architecture of one-dimensional convolutional neural network and gated recurrent unit to collaboratively model the local time-frequency features and long-range temporal dependence. The adversarial adaptive feature generator-discriminator network is designed, and the dynamic game mechanism is introduced to optimize the training process, and the L₂ paradigm constraints are utilized to force the potential spatial geometric consistency, effectively suppressing noise interference and realizing efficient generation of domain-invariant features. A multimodal fault classification framework is constructed and an attention-weighted nonlinear fusion strategy is adopted to dynamically integrate the changes in the time-frequency characteristics of vibration signals, which improves the classification accuracy of complex fault modes. The experimental validation on the CWRU bearing dataset shows that the model in this paper performs well in the experimental groups of C₁-C₆, C₇-C₁₂ and C₁₃-C₁₈ containing different rotational speeds (1 797、1 772、1 750 r/min) and fault degrees (0.177 8、0.355 6、0.533 4 mm), with the average recognition accuracies reaching 91.52%, 94.65% and 91.40%, which is significantly better than the comparison models of REB-ADDA, MsDCNs, SDA, and ISAMCN. In the hyper-parameter configuration with a learning rate of 0.001 and a batch size of 64, the average recognition accuracy of the C₁-C₆ group is as high as 98.7%, which is an improvement of 6.2% over the optimal baseline model, and the highest precision rate of 98.5%, recall rate of 98.2%, F1-score of 98.3% and other indicators are outstanding. The t-SNE visualization results clearly show that the boundaries of different fault clusters are distinct, and the separation of the inner ring and rolling body fault features is significant, which effectively proves that the model's feature discriminative ability and interpretability, and provides a solution with high precision and robustness for the intelligent operation and maintenance of industrial bearings.