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Accurate and rapid identification of cow manure morphology is of great significance for monitoring and precise management of cow gastrointestinal health. In response to the problems of strong artificial dependence and difficulty in identification in current cow manure recognition methods, a method for identifying cow thin, loose, hard, and normal manure was proposed based on the VGG-ST (VGG-Swin Transformer) model. Firstly, a total of 879 images of the four different forms of manures was collected from lactating Holstein cows and augmented to 5580 images using operations such as flipping and rotation as the dataset. Then, five typical deep learning image classification models, namely Swin Transformer, AlexNet, ResNet-34, ShuffleNet and MobileNet, were selected for cow manure classification research. Through comparative analysis, Swin Transformer was determined to be the optimal base classification model. Finally, the VGG-ST model combined the VGG model with the Swin Transformer model. The VGG model was utilized to capture local features of cow manure, while the Swin Transformer model extracted global self-attention features. After feature concatenation, the cow manure images were classified. The experimental results showed that the Swin Transformer model achieved a classification accuracy of 85.9% on the testing set, which was 1.8 percentage points, 4.0 percentage points, 12.8 percentage points, and 23.4 percentage points higher than that of ShuffleNet, ResNet-34, MobileNet, and AlexNet, respectively. The classification accuracy of the VGG-ST model was 89.5%, which was 3.6 percentage points higher than that of the original Swin Transformer model. The research result provided a method reference for the development of automatic inspection robots for cow manure morphology.