Facial expressions are the most intuitive representation of human emotional states， and convolutional neural networks have shown excellent performance in facial expression recognition. However， occlusion and pose changes in complex scenes are still two major problems in automatic facial expression recognition， which significantly changes the appearance of faces and affects the final recognition results. Aiming at the problems of occlusion and pose change in facial expression recognition， a facial expression recognition method based on dual attention and multi-region detection network is proposed. Dual attention is used to improve the feature extraction capability of the overall network， enabling the network to focus on more detailed feature information. Multi-region detection is used to adaptively capture important local regions in facial expression recognition of occlusion and pose changes， and suppress the negative effects of occlusion and pose changes. Finally， the effectiveness of the proposed method is verified on three public natural scene facial expression datasets AffectNet， RAF-DB and SFEW.