In order to solve the problem of insufficient samples of some ship types in the process of constructing synthetic aperture radar (SAR) image ship target data set, a SAR ship target expansion method based on multiscale generated countermeasure network (IC-ConsinGan) is proposed. By introducing the attention mechanism into the parallel multi-stage multiscale GAN network, the key features of SAR ship targets are extracted and the background features are suppressed, so that the generated SAR image ship targets not only have a refined structure, but also make up for the lack of diversity in the process of generating a single image. The experimental results show that the SIFID index is 0. 02 lower than that of the original ConsinGan network model, and the average recognition rate of 10 types of ship targets is improved by 8.4% when the extended data is added to the SAR ship target recognition task, which confirms the effectiveness of the IC-ConsinGan model and has certain engineering application value.