In bearing fault diagnosis, traditional deep learning methods often fall short of achieving satisfactory diagnostic accuracy under variable operating conditions. A critical phase in this process is data acquisition, which heavily relies on high-precision sensors to accurately capture the real-time operational state of the bearing ring. To address this, a diversified sensor fusion strategy has been proposed, encompassing various sensor types such as temperature, and acoustic sensors. The strategy allows for comprehensive monitoring of the bearing’s state from multiple dimensions. Vibration sensors are responsible for detecting minute vibrations and abnormal vibration patterns during the bearing’s operation. Temperature sensors monitor changes in the bearing ring’s temperature to identify potential overheating issues, whereas acoustic sensors capture unusual noises that may indicate faults. From the collective data gathered by these sensors, a comprehensive view of the bearing’s operational state can be obtained, significantly enhancing the accuracy of fault diagnosis. To tackle the issue of low diagnostic accuracy under variable working conditions, an algorithm combining the advantages of data reconstruction and discriminative space optimization, data deconstruction and meta-learning discriminative space optimization (DR-MLDSO), has been utilized. Additionally, by integrating a hybrid dual-channel attention mechanism into the feature extraction network, challenges arising from variable application scenarios and data quality issues have been effectively addressed. Faced with the challenge of insufficient sample size, a similarity-based meta-learning algorithm was employed to train the encoder. Furthermore, the introduction of new constraints in the loss function leads to an improved sparse denoising autoencoder that optimizes data reconstruction, effectively reducing noise while preserving key features. Finally, incorporating a self-attention mechanism enhances the model’s diagnostic capabilities in noisy environments, achieving superior diagnostic performance under variable working conditions, even with small sample sizes.