【Objective】To address the severe performance loss after model pruning,a compression algorithm based on filter attenuation and self-knowledge distillation is proposed. [Method] This method utilized a filter attenuation mechanism to preserve the information of redundant filters, thereby minimizing the model difference before and after pruning, and reducing the performance loss caused by pruning. Meanwhile, an annealing attenuation function was introduced during the pruning process to dynamically change the attenuation of filters, enabling a fast and efficient search for the optimal substructure of the model and improving the convergence speed of the model. Additionally, self-knowledge distillation was employed for knowledge transfer between the pre-trained model and the compressed model. [Result] The results show that this compression algorithm improves the model accuracy by 0.12 percentage points while reducing the FLOPs of the VGG-16 model by 37.3% . [Conclusion] This method provides a more stable and efficient model compression approach for convolutional neural networks.