This study addresses the challenges of low accuracy and efficiency in the detection of wildlife at night， as well as the difficulties associated with manual comprehensive labeling. An end-to-end recognition model for nighttime wildlife based on semi-supervised learning（SAN-YOLO） was proposed and investigated. A feature attention mechanism and a pixel attention mechanism were integrated within the YOLOv8 framework to enhance the adaptability and feature representation capabilities of the detector for nocturnal images. Subsequently， a semi-supervised training network based on a teacher-student learning paradigm was constructed， allowing the student model to learn from a substantial number of unlabeled original images by generating and appropriately assigning pseudo-labels. The efficacy of the constructed dataset was then evaluated. Experimental results demonstrate that the mean Average Precision （mAP） of SAN-YOLO reaches 69.7% with only 5% annotated data， surpassing the 59.6% mAP achieved with full supervision in its conventional detector and exceeding the baseline model's performance of 57.1%. Consequently， the proposed detection method exhibits robust performance with a limited number of labeled datasets for nocturnal animals and validates the effectiveness of attention mechanisms in the domain of nighttime object detection.