Synthetic Aperture Radar (SAR) system owns all-weather imaging characteristics and important application value in water body detection, but there still exists the problem of difficulty in extracting the features of fine tributary water bodies when extracting the information of multi-scale water bodies. The article proposes a network called Multi-scale Attention LinkNet (MATLinkNet). The network is mainly divided into two parts: encoder and decoder. In the initial block stage before the encoder, multiple small convolutions are used instead of the traditional 7×7 convolution, which can extract more delicate water body information. Subsequently, the Attentional Multi-Scale Pyramid (AMSP) module with the attentional mechanism is constructed in the encoding stage to enhance the learning of water body features at different scales and focus on the important features of the water body. Finally, skip connections are designed to chain the features of the encoder and decoder to compensate for the loss of spatial information caused by multiple downsampling in the encoding stage, which effectively improves the extraction accuracy of the water body and reduces the training time at the same time. Experiments are conducted on the self-made Sentinel-1 SAR image water body dataset, and the independent test results show that the highest values of water body extraction accuracy and intersection and concatenation ratio reach 90.73% and 81.95%, respectively, which are 6.81 and 5.27 percentage points higher than that of the original LinkNet network, verifying the network’s excellent performance in the task of segmentation of SAR water body images.