To solve the problem of adhesion objects and data distribution deviations in few-shot scenarios, a synthetic aperture radar (SAR) object detection method based on meta-learning is proposed, which includes support feature guidance block and variational inference block. The former enhances the key features used for bounding box positioning in the query feature, so that the module can generate accurate proposals even in face of the adherent SAR objects. On this basis, to correct the deviation of the data distribution caused by the few-shot data, a variational inference block is constructed to map the supporting features to the class distribution in the hidden space. To fuse robust class-level features, meta-knowledge is used to calculate the distribution of the support feature classes of classes. The proposed algorithm uses a few-shot support set data to migrate priori knowledge to a class using the few-shot tasks and data double sampling. Moreover, a few-shot SAR object detection dataset is established to verify the effectiveness of the proposed method, and the experimental results show that our method has obvious advantages over the representative few-shot SAR object detection algorithms.