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Depth images are increasingly used to detect the body condition of dairy cows to make breeding management decisions. The scoring method of individual dairy cows’ body condition based on deep learning can further improve the degree of automation of dairy cow body condition image analysis. In order to realize the accurate recognition of the individual body condition of dairy cows without contact, high accuracy and strong applicability based on depth images in the actual breeding environment, a body condition scoring method was proposed based on deep learning and point cloud convex hulling features. Firstly, the acquired back depth image of the cow was preprocessed, included target extraction, target rotation, and acquired hindquarters images to obtain a back point cloud of the cow, containing the main body condition information. And then the hindquarters point cloud was voxelized and the convex hull feature image was obtained. In order to represent the fat and thin degree of different cows, and finally build a variety of convolutional neural network classification models, accuracy rate and average F1 value was used to optimize the model to further improve the accuracy of individual body condition recognition of dairy cows. The test results showed that the EfficientNet network can effectively identify the body condition of cows with a BCS value in the range of 2.25~4.00. The image account of recognition accuracy errors of 0.25 and 0.50 were 98.6% and 99.31%, respectively. The average F1 value was 98% and 99%, and the average recognition rate was 3.441s/f. Compared with the MobileNet-V2, XceptionNet, and LeNet-5 network models, the above indicators of the proposed method were better. The method can realize the non-contact assessment of the individual body condition of dairy cows in the breeding farm, and had the characteristics of high accuracy, strong applicability, and low cost.