Abstract: Although deep neural network technology brings high recognition accuracy to the field of synthetic aperture radar image-based automatic target recognition, it has also caused catastrophic forgetting problems during the continuous sample training process. Currently, academia uses incremental learning methods to alleviate the catastrophic forgetting problem in the continuous learning process of deep neural networks. The key problem of incremental learning is to extract and retain the features used to distinguish new classes from old classes, which has also become the main bottleneck for improving incremental learning performance. Mainstream incremental learning methods generally retain key old class features by selecting and retaining a certain number of old samples. In order to further improve the performance of incremental learning methods and enhance the practicality of incremental learning, this paper proposes a new incremental learning sample retention method, which retains old samples with stronger representativeness of old class feature. We utilize the K-means method to select representative old samples, and then use the distillation loss to train the new model. Available from experiments on the MSTAR dataset. Our method can further improve the incremental learning ability of neural networks for SAR images.