Mining bearing fault characteristics in high-power variable-frequency wind turbines is challenging, and existing deep learning models suffer from poor interpretability. To address these issues, a new intelligent diagnosis framework of lightweight space-time information fusion model, named BSTA-Net, is developed to enhance bearing fault identification in practical engineering applications. Firstly, a bearing fault feature space-time information fusion module is designed, and a new bidirectional timing information feature fusion strategy is creatively developed. The strategy is cleverly applied to the proposed BSTA-Net framework to fully extract the fine-grained features from the fault data, marking the first attempt to apply such an approach to wind turbine bearing condition monitoring. Secondly, the feature focusing module is introduced into the proposed framework for optimization, enabling it to effectively prioritize critical fault-related information while discarding irrelevant or noisy features. This ensures that the model maintains robust learning capabilities even under complex conditions such as alternating voltage shocks and variable loads. Finally, based on the same data set, the diagnostic performance of 8 methods such as BSTA-Net framework is compared from multiple dimensions, and the diagnostic results are compared with 7 methods such as BST-Net. The results show that the proposed framework exhibits superior superiority and strong generalization ability, providing a new idea for bearing fault identification. Furthermore, T-SNE and significance region detection technology are introduced into the BSTA-Net framework to explain the physical attribution of fault feature mining process, thereby improving the reliability of the framework in the decision-making process.