Aiming at the problem that the current fabric automatic opener can’t accurately identify the opener guide line under the complex fabric texture background, this paper designs a fabric texture feature classification system based on GoogleNet network of transfer learning and residual network of nontransfer learning. In this paper, the sample data set is divided into seven texture characteristics, such as dense pattern, a total of 1543 images, randomly selected 80% of the images as the training set, the remaining 20% of the images as the test set. Two different convolutional neural networks achieve 100% recognition accuracy in the existing data set, and in the subsequent system test, 300 samples are added, and the final recognition accuracy of the system reaches 98%. The experimental results show that the application of googlenet network and residual network to the analysis and classification of fabric texture characteristics is feasible, and the system based on this algorithm has practical value.