Contrastive learning， as a self-supervised approach， enables the extraction of target representations from unlabeled SAR images， serving as a critical technique for automatic target recognition （ATR） in SAR. However， existing models often encode targets and backgrounds holistically， resulting in feature representations contaminated by background interference， which diminishes the model’s ability to focus on targets. To address this issue， this paper proposes a novel multi-branch dual contrastive learning model. Firstly， the model retains the conventional instance contrastive branch while introducing an innovative background correction contrastive branch， establishing a multi-branch contrastive learning framework. Secondly， through a random recombination strategy of targets and backgrounds in positive and negative samples， combined with the ResNet50 backbone network and self-attention pooling to enhance semantic feature extraction， an optimized dual contrastive loss function is employed to refine target feature learning and mitigate spurious correlations between backgrounds and targets. Finally， Shapley value analysis based on the MSTAR dataset validates the model’s effectiveness， and target classification results demonstrate that this approach significantly enhances the causality of feature extraction， substantially improving the generalization performance of SAR ATR algorithms.