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The lying rate of dairy cows can reflect the comfort and health of dairy cows. The individual identification of lying cows is the basis of automatic monitoring of lying rate. A method based on the improved YOLO v4 model to identify individual lying cows in an unconstrained barn environment was proposed. Firstly, in order to realize accurate individual identification of lying cows throughout the day, MSRCP algorithm was used to enhance the images from 18:00 to 07:00 the next day, which improved the image quality in low light environment. Secondly, the RFB-s structure was integrated into the backbone network of YOLO v4 model to increase the robustness of the model to the changes of cow body patterns. Finally, in order to improve the identification accuracy rate of cows with similar patterns, the non-maximum suppression (NMS) algorithm of YOLO v4 model was improved. The experiment of cow individual identification was carried out on the image data set of 72 cows. The results showed that the precision, recall, mAP, and F1 values of the improved YOLO v4 were 97.84%, 93.68%, 96.87%, and 95.71%, respectively. The improved YOLO v4 model was compared with the YOLO v4 model, the precision, recall, mAP and F1 values of the improved YOLO v4 were increased by 4.66 percentage points, 3.07 percentage points, 4.20 percentage points and 3.83 percentage points, respectively, without reducing the processing speed. The mAP of the improved YOLO v4 was higher than that of YOLO v5, SSD, CenterNet and Faster R-CNN by 8.52 percentage points，15.22 percentage points, 12.18 percentage points and 1.55 percentage points, respectively. The method can provide an effective technical support for the health monitoring of dairy cows in precision dairy farming.