A migration learning network model based on the ResNet50 model was studied for accurate and fast classification of the manufactured casings. By constructing a neural network model as well as obtaining sausage casing samples from a cooperative factory and making a total of 2 000 data sets of four grades A, B, C and D according to the actual quality. A new fully connected layer is designed based on the ResNet50 model. And divided into training set and test set in the ratio of 7∶1. Experimentally, it can be seen that the accuracy of migration learning is 99% far better than the accuracy of 94% of the ordinary deep learning model, and the accuracy is significantly improved. Finally, the trained model is made into a user interface using pyqt, a Python graphical tool, for practical application. The migration learning-based intestinal coating quality detection system established in this study can achieve fast and accurate classification of intestinal coating quality, reduce labor cost, and provide a basis for future intestinal coating quality detection.