Abstract: The purpose of one-shot learning is to use a source category dataset containing a large number of training samples and a target category dataset containing only one training sample per category to construct a learning algorithm that enables accurate classification of samples in the target category space. The existing one-shot learning algorithm mainly uses the source category data to train the model, and then uses the training data of the target category as the support set to realize the classification of the unlabeled samples during the test. Therefore, it fails to effectively utilize the information of the support set during the training. Here, a one-shot learning algorithm using support set information in both the training and test stages is established. The basic idea is to use Siamese neural networks to build models and add support set information during training, that is, to make the similarity between different types of support set samples as small as possible. Experimental results on Omniglot data set and Manchu recognition show that the proposed algorithm can achieve better recognition accuracy.