Facial expression recognition is an important research direction of emotion computing and has broad application prospects in human-computer interaction. However, noise such as illumination and occlusion in natural environment brings many challenges to facial expression recognition. In order to solve the problems such as low recognition rate of facial expression in natural environment, unable to highlight the characteristics of facial expression in global facial research, and misclassification caused by the similarity between negative expressions. In this paper, a multi-region coordinate attentional residual expression recognition model (MrCAR) is proposed. The model is mainly composed of the following three parts: 1) multi-region input: MTCNN is used for face detection and alignment processing, and the eyes and mouth parts are further cropped to obtain multi-region pictures. Through multi-region input, local details and global features are more easily obtained, which reduces the influence of complex environmental noise and highlights the facial features. 2) Feature extraction module: On the basis of residual element, CA-Net and multi-scale convolution were added to obtain coordinate residual attention module, through which the model's ability to distinguish subtle changes of expression and the utilization rate of key features were improved; 3) Classifier: Arcface Loss is used to enhance intra-class tightness and inter-class difference at the same time, thus reducing the wrong classification of negative expressions by the model. Finally, the accuracy rates of CK+, JAFFE, FER2013 and RAF-DB were 98.78%, 99.09%, 74.50% and 88.26%, respectively. The experimental results show that compared with many advanced models, the MrCAR model in this paper is more competent for the task of expression classification.