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Facial keypoint detection in dairy cows plays a crucial role in the automation of cow farms. It aids in cow face recognition, face alignment, head movement detection, and behavior recognition. In view of the problems of cow face occlusion and weak light in the current dairy farming environment, an improved algorithm of YOLO v7-Pose network model was proposed, which can be used for keypoint detection and head pose recognition of cow face. Firstly, dairy cow facial images were collected from cow farms by using network cameras and a dataset was constructed. Secondly, the SPPFCSPCL structure was integrated into the YOLO v7-Pose network model to enhance its feature extraction capabilities for cow facial keypoints. The WingLoss loss function replaced the OKS loss function for keypoint detection, thereby improving the accuracy of cow facial keypoint detection. Finally, L1 regularization was applied to prune the improved model, reducing the number of network parameters. The experimental results showed that the cow face keypoint detection of improved model YOLO v7-SCLWL-Pose was improved by 5 percentage points and AP0.5 was improved by 2.7 percentage points compared with the original model AP, and the memory occupation of the improved model was only 106.7MB, which was reduced by 33.6%. The keypoint detection was applied to pose recognition, and the experimental results showed that the recognition accuracy of the motions of looking up and looking down reached 95.5% and 86.5%. This research can provide support technology for behavior recognition in dairy cows on farms.