Traditional facial expression recognition often uses a single image to extract features, train, and recognize; however, subtle changes in dynamic facial expressions are not recognized. This study proposes a multi-expression sequence fusion recognition method based on probabilistic cooperative representation using the changes in facial expression before and after time. First, 68 feature points of facial expression are located using an active appearance model (AAM). Then, the AAM features of three adjacent facial expressions are combined using the the proposed method. Finally, the classification advantages of probabilistic cooperative representation are used for recognition. Experimental results indicate that the proposed method can grasp the temporal change information of expression on the CK+ expression database. Moreover, this method can achieve higher recognition rates compared with traditional expression recognition algorithms.