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Facial expression recognition (FER) is an important means for machines to perceive human emotions and interact with human beings. Most of the existing facial expression recognition methods only use a single convolutional neural network to extract the global features of the face. Some insignificant details and features with low frequency are easy to be ignored, and part of the facial features are lost. This paper proposes a facial expression recognition method based on multi branch structure, which extracts the global and detailed features of the face from the global and local aspects respectively, so as to make a more detailed representation of the facial expression and further improve the accuracy of facial expression recognition. Specifically, we first design a multi branch network, which takes Resnet-50 as the backbone network. The network structure after Conv Block3 is divided into three branches. The first branch is used to extract the global features of the face, and the second and third branches are used to cut the face into two parts and three parts after Conv Block5 to extract the detailed features of the face. Finally, the global features and detail features are fused in the full connection layer and input into the classifier for classification. The experimental results show that the accuracy of this method is 73.7%, which is 4% higher than that of traditional Resnet-50, which fully verifies the effectiveness of this method.