Since synthetic aperture radar (SAR) can observe all-weather, it is widely used in ship target detection and segmentation tasks. However, SAR images have complex backgrounds and clutter interference, which affect the segmentation accuracy. This paper proposes a saliency-combined complex-valued U-Net. The network consists of two parts, namely complex-valued U-Net(CV-UNet) and original U-Net. The CV-UNet is used to process the measured data of SAR images which contains amplitude and phase information. The original U-Net is used to process the saliency map generated by the SAR image, and the results of two parts of the network output are connected. The experiment uses the measured data of HISEA-1 to make a target segmentation dataset, and uses the trained network for testing. The results show that the performance of the proposed method is better than that of the original U-Net and CV-UNet.