Abstract: In order to solve the problems of large model and slow recognition speed in individual recognition of dairy cows in large-scale intelligent dairy farms, a Multi-Light model was proposed for individual identification of dairy cows. The single cow image was segmented from the complex background of the tagged cow image by using the DeepLab V3 semantic segmentation network. In the Multi-Light model, dilated convolution was introduced to ensure the ability to extract the global information of the image while keeping the number of parameters unchanged. A Multi-scale convolution module was added to enhance the detection ability of the model of feature points at different scales by the model. In the model, shortcut was used to ensure that the features were not lost and the recognition accuracy of the model was improved. In addition, channel attention was used to improve the recognition accuracy of the model and make the model more nonlinear. Finally, the segmented cow image data set was input into the Multi-Light model for training. The results showed that the average recognition accuracy of Multi-Light model for individual cow recognition was 98.51%, which was higher than that of other classical models. Compared with the lightweight model, the size of Multi-Light model was 5.86 mb, and the number of parameters was less on the premise of high recognition accuracy. The results indicated that the lightweight model constructed in this experiment overcame the defects of traditional methods, such as the need for artificial extraction of features, the lack of robustness of feature extraction methods, and the large amount of recognition model parameters and slow recognition speed, and provided a reference for individual lightweight recognition of dairy cows.