Abstract: Insufficient labeled samples and diverse viewing angles are two major challenges that constrain the performance of synthetic aperture radar (SAR) target recognition. To address these issues, a hybrid supervised contrastive learning framework tailored for few-shot multi-view SAR image recognition is proposed. The proposed method divides a training process into two stages: an upstream representation learning stage and a downstream classifier learning stage, incorporating a multi-view feature fusion strategy to exploit complementary information across different viewing angles. In the upstream stage, various random augmentation operations are applied to SAR image patches to enlarge the effective batch size and construct diverse positive and negative sample pairs. These augmented samples are fed into a shared multi-view fusion network and a projection head, which maps the features into a contrastive space. A hybrid supervised contrastive module then jointly performs unsupervised instance discrimination and supervised label discrimination, improving both the discriminability of the feature space and the intra-class compactness. In the downstream stage, the projection head is removed, only a linear classifier is trained to perform the final recognition task. Experimental results on the few-shot moving and stationary target acquisition and recognition dataset demonstrate that the proposed method achieves significant improvements in recognition accuracy and generalization ability over conventional supervised and some unsupervised learning methods. These results validate the effectiveness and application potential of the proposed model in few-shot multi-view SAR recognition scenarios.