Abstract: Traditional manual measurement of RQD is time-consuming and labor-intensive with a low level of automation, and the performance and accuracy of image-based RQD recognition technology in engineering applications still require improvement. In view of the above problems, this paper puts forward an automatic and accurate identification rock mass method for RQD based on improved yolov8-seg. This method builds on the YOLOv8-seg framework and introduces a lightweight multi-semantic space and channel collaborative attention module, SCSA, to accurately extract borehole core features despite the interference from shooting conditions and manual markings, while incorporating dynamic serpentine convolution to enhance the model's ability to detect small cracks, thereby achieving effective extraction of core sections. Furthermore, based on the image segmentation results of core sections, RQD is automatically calculated and recorded. The case analysis shows that compared with the original model, the accuracy P, recall R, mAP50 and mAP50:95 of the improved YOLOV8-seg model have increased by 3.3%, 2.2%, 2.4% and 1.9%. The proposed method can achieve the automatic and accurate identification of RQD, and the average relative error is 1.21%.