In order to solve the problem of low recognition accuracy caused by insufficient illumination and occlusion, a convolutional neural network model based on transfer learning was constructed. Based on the analysis of yarn quality index, the classification standard of the expression of the spinner was determined, and the expression data set was established. At the same time, the data set was preprocessed by histogram equalization, ROF (Rudin-Osher-Fatemi) denoising and facial correction. On the basis of intercepting the real-time eye data of the spinner, the transfer learning method was used to train the expression recognition model. Finally, through the experimental verification, it is shown that the recognition accuracy of the proposed model was as high as 98%, which effectively solves the problem that the spinner expression can not be recognized due to illumination and occlusion.