Memristors have the advantages of nanoscale size, low power consumption and similar to neural synapses, etc., and have broad application prospects in neural computing, image classification and other fields. In this paper, a facial expression recognition method based on memristor-based convolutional neural network is proposed. First, a memristor-based ResNet convolutional neural network is constructed and the ResNet network is pruned. Then the weights of all convolutional layers and fully connected layers of the ResNet model are mapped as the memductance values of memristors in the memristive crisscross array. The experimental results show that the recognition accuracy of the memristor-based convolutional neural network model on the FER2013 dataset is 63.82%, and the recognition accuracy on the CK+ dataset is 93.95%. Compared with the original convolutional network, the accuracy loss is only 0.31% and 0.76% respectively. Finally, the influence of the non-ideal characteristics of the memristor on the accuracy is tested, which provides a reference for the actual deployment of the memristor-based neural network.