At present, deep learning has surpassed humans in recognition tasks, but its performance strongly depends on sufficient training samples. When the training samples are extremely insufficient, overfitting also will damage the performance of network, despite the use of fine-tuning methods. One-shot learning (OSL) is proposed to overcome the difficulties of training neural networks while having almost no samples, which is a common situation in radar image recognition. This paper introduced OSL into recognition of some radar targets whose labelled images are seriously limited, and proposed an OSL method with prototypical networks based on the assumption that functions of the network trained with auxiliary samples also affects samples to be identified. The method of this paper involves image preprocessing, building of linearly separable feature space and feature vector clustering. Experiments on MSTAR dataset show that the performance of our method is better than deep transfer learning when the training samples in the target domain are seriously deficient.