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In order to accurately and efficiently realize the contactless individual identification of dairy goats, a dairy goat individual identification method based on improved YOLO v5s was proposed by taking the facial images of dairy goats in captive environment as the research object. Firstly, totally 350 sheep face images were randomly collected from the network to form a sheep face facial detection dataset, and the YOLO v5s model was pre-trained by using the transfer learning idea to enable it to detect sheep face positions. Secondly, a facial image dataset was constructed, containing 3844 different growth stages of 31 dairy goats, based on pretrained YOLO v5s, SimAM attention module was introduced in the feature extraction layer to enhance the learning ability of the model, and CARAFE was introduced in the feature fusion layer. The sampling module can better restore facial details and improve the recognition accuracy of the model for individual faces of dairy goats. The experimental results showed that the average accuracy of the improved YOLO v5s model was 97.41%, which was 6.33 percentage points, 8.22 percentage points and 15.95 percentage points higher than that of the Faster R-CNN, SSD and YOLO v4 models, respectively, and 2.21 percentage points higher than that of the original YOLO v5s model. The detection speed of the improved model was 56.00f/s, and the model size was 14.45MB. The method proposed can accurately identify dairy goat individuals with similar facial features, which provided a method support for the identification of livestock individuals in smart farming.