In order to improve the average accuracy of loudspeaker abnormal sound classification, a convolutional neural network plus gated current unit (CNN-GRU) and sparrow search algorithm optimization variational modal decomposition ( SSA-VMD) model was proposed to classify loudspeaker abnormal sound. In the aspect of feature extraction, the SSA-VMD model was used to determine the optimal value of the second penalty factor (α) and modal decomposition number (k) in VMD, so as to improve the accuracy of feature extraction and reduce the extraction time. Finally, the VMD was used to extract the characteristics of the loudspeaker response signal. In terms of classification network, the CNN-GRU network was used to classify the abnormal sound of loudspeakers, the CNN-based feature extraction network was used, and the GRU network was used for deeper feature extraction to achieve the goal of improving the average classification accuracy of loudspeakers. The experimental results show that after optimizing the parameters of SSA-VMD model, VMD can extract features more effectively, and the decomposition time was reduced by 59. 8%. The CNN-GRU model has a higher and more stable recognition rate, with an average classification accuracy of 99. 2%.