Facial expression recognition plays an increasingly important role in monitoring the mental state of individual soldiers. Aiming at the problem of low accuracy rate by traditional facial expression recognition method, this paper proposes an improved shallow residual network facial expression recognition algorithm. Based on ResNet which is the main target detection framework of existing deep learning, a Res-HyperNet structure is proposed to learn more shallow features. First, the shallow features are aggregated and compressed into one space. To improve the recognition effect, this paper sent the high-level features which have more useful semantic information to a deeper convolution layer through a shortcut to perform fusion calculation. Three public datasets (CK+, JAFFE, Oulu-CASIA) and self-made datasets are used for accuracy rate experiments. Compared with the existing mainstream facial expression recognition method, the improved shallow residual network algorithm can raise the accuracy rate up to 3%.