When the deep learning algorithm is applied to the field of SAR image classification, there are some problems such as long model training time and low accuracy.In order to solve the problems, a convolution neural network model based on hybrid attention mechanism is proposed.The basic module of the model is divided into trunk branch and soft branch.The trunk branch is composed of residual shrinkage network and the improved channel attention mechanism, which is responsible for extracting the main features.The soft branch combines down sampling with up sampling to extract the hybrid attention weight and enhance the mapping ability from input to output.The recognition rate of the model is 99.6% on MSTAR data set, and the training time is short.Noise analysis shows that the model is robust to salt and pepper noise.