Construction sites are often crowded, with poor working environments, many sources of danger, and hazardous areas. Unauthorized entry into these areas poses significant safety risks and can lead to accidents. Traditional construction site monitoring relies on full-time safety officers patrolling the site, which is time-consuming, labor-intensive, and results in untimely warnings. To improve site monitoring efficiency and make full use of existing cameras, this paper proposes a construction hazardous area intrusion detection method based on binocular stereo vision technology and deep learning. First, relevant literature is reviewed to determine the methods for establishing and dividing dangerous areas. A target detection algorithm is then used to locate construction personnel and machinery, and binocular stereo vision technology calculates the distance between them. The method for determining the man-machine distance is improved to judge whether there is intrusion into dangerous areas based on the detected distance. A comprehensive construction site simulation experiment verifies the method, and results show that it accurately detects intrusion behavior and provides early warning information, significantly improving site monitoring efficiency and safety.