In order to solve the problem that the existing SAR target detection algorithm only uses the underlying features of the image for detection, and the detection rate of small-scale ship targets is low, an object detection algorithm combining feature fusion and attention mechanism was proposed. For SAR ship-target detection, on the basis of the original backbone network SSD target detection algorithm, the attention mechanism module is introduced, the feature maps at different levels are fused with features, the images containing small-scale targets are oversampled, and the data augmentation is achieved by copying and pasting small targets multiple times. Through a large number of training and testing of SAR ship image datasets, the results show that the proposed algorithm can effectively improve the comprehensive detection performance of ship targets, and the mean average precision can reach 94.16% on the public SAR ship target detection dataset.