INTRODUCTION: The facial coloration of sheep is not only a critical characteristic for breed and individual identification but also serves as a significant indicator for assessing genetic diversity and guiding selective breeding efforts. METHODS: In this study, 201 Ujumqin sheep were used as research objects and 1713 head image data were collected. We delineated feature points related to the facial coloration of Ujumqin sheep and successfully developed a head color recognition model (YOLOv8-CBAM) utilizing the YOLOv8 architecture in conjunction with the CBAM attention mechanism. RESULTS: The model demonstrated impressive performance in recognizing four head color categories, achieving an average precision (mAP) of 97.7% and an F1 score of 0.94. In comparison to YOLOv8n, YOLOv8l, YOLOv8m, YOLOv8s, and YOLOv8x, the YOLOv8-CBAM model enhances average accuracy by 0.5%, 1%, 0.7%, 0.7%, and 1.6%, respectively. Furthermore, when compared to YOLOv3, the improvement is 1%, while YOLOv5n and YOLOv10n show increases of 1.4% and 2.4%, respectively. DISCUSSION: The findings indicate that the smaller model exhibited superior performance in the facial color recognition task for Ujumqin sheep. Overall, the YOLOv8-CBAM model achieved high accuracy in the head color recognition task, providing reliable technical support for automated sheep management systems.