Abstract: To address challenges such as severe dust and fog interference, complex background environments, and variable personnel scales with frequent occlusions in coal mine belt conveyor scenarios, which resulted in low accuracy in recognizing personnel intrusions into hazardous areas, an intelligent recognition system based on an improved YOLOv8 model was proposed. The improved YOLOv8 model enhanced detailed feature extraction by replacing the C2f module in the backbone network with the C2fER module, which improved recognition performance for small targets. The Feature Enhancement Weighted Bi-Directional Feature Pyramid Network (FE-BiFPN) structure was introduced into the neck network to strengthen feature fusion capabilities, thereby enhancing recognition of multi-scale personnel targets. The Separated and Enhancement Attention Module (SEAM) was incorporated to improve the model's attention to local features in complex backgrounds, which boosted its ability to recognize occluded personnel targets. Furthermore, the WIoU loss function was applied to enhance training outcomes, improving recognition accuracy. Ablation experiment results showed that the improved YOLOv8 model achieved a 2.3% increase in accuracy and a 3.4% improvement in mAP@0.5 compared to the baseline YOLOv8s model, with a recognition speed of 104 frames per second. Personnel recognition experiments demonstrated that, compared to YOLOv10m, YOLOv8s-CA, YOLOv8s-SPDConv, and YOLOv8n models, the improved YOLOv8 model delivered superior recognition performance for small, multi-scale, and occluded targets, achieving a recognition accuracy of 90.2% and an mAP@0.5 of 87.2%. Personnel intrusion experiments revealed that the intelligent recognition system achieved an average accuracy of 93.25% in identifying personnel intrusions into belt conveyor hazardous areas, satisfying recognition requirements.