With the continuous development of deep learning, a large number of deep learning models are gradually emerging in the field of SAR target recognition. However, SAR targets usually have complex textures and noises, and it is difficult to directly extract effective feature information and rich contextual information between the target object and the background, which inhibits the potential of deep learning models to further improve the recognition ability in the SAR domain. To ameliorate this problem, a GIC-Mechanism that improves the ability to capture global and local feature information interactively is proposed in this paper and applied to the Resnet family of models. In the recognition task of two SAR target datasets, the mechanism designed in this paper with the ability of multi-scale losing feature information interaction and cross-feature mapping layer information interaction improves the recognition performance of the Resnet series model by 2.16%-2.81%, which is effective.