Abstract: To address issues such as background interference caused by oil stains covering the surface of mine hoisting steel wire ropes, large gaps between rope strands leading to feature confusion, and the difficulty in identifying small target damages, a surface damage image recognition method for mine hoisting steel wire ropes based on an improved YOLOv8n model was proposed. The Multi-Scale Attention Module (MSAM) was introduced into the YOLOv8n backbone network to enhance the model’s ability to distinguish between damage features and oil stain backgrounds, improving its anti-interference capability. The original three detection heads of YOLOv8n were replaced with four lightweight small-target detection heads to strengthen the recognition ability for small target damages. Depthwise Separable Convolutions (DSConv) were used instead of standard convolutions to reduce computational load and improve recognition speed. Experimental results showed that the improved YOLOv8n model achieved an Mean Average Precision (mAP) of 92.6%, a recognition accuracy of 89.7%, and an inference speed of 43.5 frames per second, representing improvements of 3.1%, 4.9%, and 34.7%, respectively, compared to the original YOLOv8n model. Compared with mainstream models such as Faster-RCNN, YOLOv5s, YOLOv8n, YOLOv10m, TWRD-Net, and YOLOv5-TPH, the improved YOLOv8n model exhibited the best accuracy for small target damage recognition while maintaining high real-time performance. In complex field scenarios with oil stain coverage and large gaps between rope strands in coal mines, the improved YOLOv8n model did not miss any damage detections and had fewer false detections, achieving an average recognition accuracy of 90%.