Facial expression recognition is a challenging task for neural network applied with pattern recognition. Moreover, feature extraction is particularly important in the process of facial expression recognition. In this paper, a attention-split convolutional residual network was proposed to enhance the feature expression. This network used ResNet18 as the backbone network and replaced the basic block in ResNet18 with the coordinate attention-split convolutional block (CASCBlock), which is also proposed here. In the CASCBlock, two split convolutions were initially used to split and then fuse the features in the channel dimension to reduce redundant feature representations. Then, the coordinate attention mechanism was incorporated after the second split convolution. Finally, a fully connected classifier was developed for expression recognition. The proposed method was tested on the FER2013 and RAF-DB datasets, and the experimental results showed that the recognition accuracy of the proposed method on FER2013 and RAF-DB datasets is 2.897 percentage points and 2.575 percentage points higher than that of ResNet18, and the number of model parameters decreased by ~60% compared with ResNet18.