Ship target identification at sea is a crucial part of maritime monitoring and a significant solution for national security in coastal regions worldwide. Aiming at the problems of low recognition accuracy and large training model in ship target detection in SAR images, an improved YOLOv7-tiny maritime ship recognition algorithm based on channel pruning is proposed. Firstly, the original backbone network is replaced by MobileNetV3 to reduce the calculation and volume of the model and realize the lightweight of the model. Secondly, MPDIoU is introduced to simplify the calculation process and optimize the convergence of the model. Finally, through channel pruning, the model accuracy is improved, while the reduction of model volume and calculation amount is balanced, and the network model is further optimized. The experimental results show that compared with YOLOv7-tiny, the improved algorithm improves the recall and mAP by 5.85 and 3.69 percentage points respectively, the parameter is reduced by 63.35%, and the FLOPs is reduced by 70%.