Facial expression recognition plays an important role in artificial intelligence such as human-computer interaction.However,current researchers ignore the semantic information of human faces.In this paper,we propose a facial expression recognition network fusing local semantic and global information,which consists of two branches:the local semantic region extraction branch and the local-global feature fusion branch.Firstly,the face semantic parsing is achieved by training semantic segmentation network on face parsing dataset.The semantic parsing of facial expression dataset is obtained by transfer training.Then the meaningful regions and their semantic features are extracted and fused with the global features to obtain the semantic local features.Finally,the global semantic composite features of facial expressions are constructed by combining semantic local features with global features.They are classified into one of the 7 basic facial expressions by the classifier.We also propose a training strategy of unfreezing partial layers,which makes semantic features more suitable for facial expression recognition and reduces the redundancy of semantic information.The average recognition accuracy on two public datasets,JAFFE and KDEF,reaches 93.81% and 88.78%,respectively.The performance outperforms the current deep learning methods and traditional methods.The experimental results demonstrate that the network proposed can describe the expression information comprehensively by integrating local semantic and global information.