To achieve real-time effects of the human behavior recognition network on the embedded platform, a human behavior recognition technique based on the lightweight OpenPose model is proposed. This approach begins with the viewpoint of 18 human body bone key points and calculates the behavior type based on the spatial position of the bone key points. First, the lightweight OpenPose model is used to extract the 18 bone key points to coordinate information about the human body. Then, the key point coding is used to describe the human body behavior. Finally, the classifier is used to classify the acquired key point coordinates to detect the human body behavior status and transplant it into Jetson Xavier NX equipment using a monocular camera for testing. Experimental results show that this method can quickly and accurately identify 11 types of human behaviors, such as walking, waving, and squatting, on the embedded development board Jetson Xavier NX, with an average recognition accuracy rate of 96.08%, and detection speed of >11 frame/s. The frame rate is increased by 177% compared to the original model.