Deep learning provides a new methods for SAR ship recognition, but most current deep learning models have a large number of parameters, making them difficult to run in resource-constrained environments. Model compression is a necessary condition for their implementation. Pruning is a commonly used model compression method. When the deep neural network is pruned too much, its accuracy will be significantly reduced, and traditional fine-tuning methods cannot restore it to a higher accuracy. For this reason, focusing on SAR ships as the research object, a network compression method that combines model pruning with knowledge distillation is proposed. Firstly, the overall architecture of the collaborative compression methodis defined, which replaces the fine-tuning process in model pruning with knowledge distillation to improve the accuracy of the pruned network. Then, a student self-reflection mechanism is introduced into the traditional knowledge distillation method to further enhance the performance of the target network. Experimental results show that the pruned network restored by the proposed knowledge distillation method performs better, and the performance of the network model after collaborative compression has reached the level of mainstream lightweight networks.