In this study, MSTAR data were selected as a sample set to solve the problem of the high noise of the synthetic aperture radar (SAR) images leading to a low target recognition rate. First, the necessity of adding an attention mechanism to the network was analyzed. Subsequently, the residual shrinkage piece was introduced in the residual attention network. An experimental analysis was performed from the perspective of the recognition rate and number of parameters. Model S was obtained by improving the first stage and the output stage of the residual attention network. Consequently, the recognition rate of model S was found to be 99.6%, and the number of parameters was reduced by nearly 1/2. Image occlusion and noise processing were conducted to test the robustness of model S. Results show that model S has a strong robustness under the conditions of image occlusion, salt and pepper noise.