In view of the lack of feature extraction ability and low recognition efficiency of common convolutional neural network in facial expression recognition, this paper proposes a facial expression recognition based on multi-scale feature fusion of MobileNetV3. Firstly, MobileNetV3 was used for feature extraction to obtain high-level emotion information. Secondly, the DenseNet structure is used in the backbone network to enhance feature reuse and improve the expression ability of important facial features. Then the feature pyramid module is used to fully obtain the deep and shallow multi-scale fusion features of face images, so as to improve the feature extraction ability and real-time performance of MobileNetV3. Finally, the full connection layer is used to construct a classifier to classify the facial expression, so as to complete the facial expression recognition. Through experimental verification, the results show that the recognition accuracy on CK+and FERPlus datasets can reach 88.3% and 98.8%, which are improved by 2.3% and 1.5% respectively compared with the existing methods, indicating that the proposed method has good recognition effect and strong generalization.