Abstract: Traditional models trained with a small number of label samples often have low prediction accuracy and weak generalization ability and are difficult to be applied to practical production. A classification method named hard pairwise-based excitation is proposed for the few-shot image classification, including pre-training stage and meta learning stage. Pre-training stage trains the encoder on the base class dataset and used as the initial feature en-coder in the meta learning stage; In the meta learning stage, the encoder will be further optimized, and the meta training process uses the essential feature method to reduce the impact of abnormal samples on the centroid; Combining measurement learning and meta learning, a loss function named hard sample-pairs excitation is de-signed. From the perspective of sample pairs, the model is guided to expand the distance between positive and negative samples during the training process, making similar samples more compact. Experiments are conducted on public datasets mini-ImageNet and tiered-ImageNet, and the classification accuracy is 64.12% and 70.15%, respectively, verifying the effectiveness and feasibility of the proposed method.