Abstract: Traditional facial expression recognition technologies rely heavily on manually formulated feature extraction rules, while deep learning-based facial expression recognition technologies can automatically perform the operations of feature extraction, feature selection and feature classification. However, for the faces with occluded parts, the existing facial expression recognition models based on deep learning cannot effectively deal with the interference of the occluded part of the face, and cannot accurately capture the features of facial unobstructed parts, thus leading to the degradation of recognition accuracy. To solve the aforementioned problems, a novel occluded facial expression recognition framework by integrating attention mechanism called FER-AM(facial expression recognition framework based on attention mechanism) is proposed, the local feature network is used to extract the local key features of facial expressions, and the global feature network is designed to learn the complementary information in the whole face, and the attention mechanism can effectively deal with facial occluded parts, such as glasses, masks and scarves. A large number of experiments are conducted on RAF-DB, AffectNet, CK+(Cohn Kanade) and FED-RO data sets, and the results show that the seven expression classification performance of FER-AM is better than the representative facial expression recognition models based on deep learning, and the recognition accuracy can reach 88.1%.