Traditional anti-bullying interventions in schools rely primarily on proactive reports from teachers and students, together with periodic inspections and surveillance by administrators. These approaches are untimely reactive and labor-intensive, which makes timely detection and response to bullying incidents difficult. A deep-learning-based campus-bullying monitoring system that provides robust protection for campus security and safeguards the mental health of students. The system integrates YOLOv8 for object detection and 3D ResNet for action recognition as its core algorithms, while leveraging servo-driven dynamic tracking as its enabling hardware technology. By combining modules for object detection, action analysis, and dynamic tracking, we construct a comprehensive and intelligent monitoring framework. By processing and analyzing live surveillance streams in real time, the system can rapidly and accurately identify potential bullying behaviors and promptly issue early-warning notifications so that administrators can intervene without delay. In addition, the system includes the “Glimmer Haven” mini-program, which offers emotional support and psychological solace to victims, alleviating their mental distress. An AI-powered chatbot further assists students and faculty in understanding and addressing campus-bullying issues, thereby fostering a safer and more harmonious school environment.