To address the challenges of noise interference, difficult fault feature extraction, and low diagnostic accuracy in rolling bearing vibration signals, this study proposes a fault diagnosis method based on differential evolution (DE)-optimized variational mode decomposition (VMD) combined with a comprehensive model integrating convolutional neural networks (CNN)-bidirectional gated recurrent unit (BiGRU)-Attention. Following the minimum envelope entropy principle, DE is employed to optimize VMD, obtaining the optimal decomposition layer number and penalty factor. The correlation coefficients of intrinsic mode functions (IMFs) under the optimal parameter combination are calculated, and useful signals are reconstructed based on a predetermined threshold. Comparative experiments with conventional classification models demonstrate that the proposed method achieves superior performance in accuracy, precision, recall and F1 score, with a fault diagnosis accuracy of 99.17%. When comparing the diagnostic results between raw and reconstructed signals in the CNN-BiGRU-Attention model, the accuracy for raw signals is 89.58%, lower than that of the denoised signals. Finally, CNN-BiGRU-Attention was compared with the CNN-bidirectional long short-term memory (BiLSTM)-Attention model. The CNN-BiGRU-Attention model showed a 1.25% higher accuracy, a 21% reduction in graphic processing unit (GPU) usage, a 23% reduction in central processing unit (CPU) usage, and a training time that was 35 seconds faster. These experimental results can provide an effective improvement method for existing rolling bearing fault diagnosis technologies.