Face expression recognition plays a crucial role in emotion recognition, human-computer interaction and other fields. The aim of this paper is to implement a deep learning-based monitoring system for real-time facial expression recognition. In this paper, a public expression dataset ExpW is used, which is a widely used public face expression dataset. The dataset contains face images from various scenarios in the real world, covering a rich variety of expressions, encompassing seven major expression categories: anger, disgust, fear, happiness, sadness, surprise, and neutrality. The YOLOv5 target detection algorithm is selected on the deep learning framework for the training and testing of the training set, which is a high-performance algorithm for fast and accurate implementation of the target detection task through a simplified network structure and optimized algorithm design. The experimental evaluation results can be concluded that the system in this paper achieves accurate expression recognition in real-time scenarios. The results of this paper show that real-time face expression recognition monitoring based on deep learning has potential applications in emotion recognition and human-computer interaction.