In order to solve the problem that the Capsule network increases the amount of calculation and the number of parameters increases sharply with the input picture, the Capsule network is improved and the improved Capsule network is used in SAR automatic target recognition (SAR-ATR). In this paper, based on the mechanism of brain visual cortex processing information in hierarchical structure and column form, the idea of complete instantiation was proposed, and the brain-like calculation was used to improve the Capsule network. The specific method was to use multiple convolution layers to achieve hierarchical processing. The number of convolution kernels used in each layer increases with the depth of the hierarchy, which made the extracted abstract features gradually increase. In the PrimaryCaps layer, the Capsule vector consisted of all the feature maps output by the last layer of the convolutional layer, so that the Capsule unit contained all the features of the target part or the whole to achieve full instantiation of the target. On the SAR-ATR, a comparison experiment was performed with the Capsule network, the traditional target recognition algorithm and the target recognition algorithm based on the classical convolutional neural network. The experimental results show that the improved Capsule network training parameters and calculations are greatly reduced, and the training speed is greatly improved, and the recognition accuracy on the SAR image data set is increased by 0.37 and 1.96-8.96 percentage points compared with the Capsule network and the first two methods respectively.