Timely diagnosis and intervention for potential diabetic retinopathy patients is very positive in improving the overall visual quality of diabetic patients and reducing medical costs. Because the fundus fluorescence images of preclinical diabetic retinopathy and normal people have no obvious difference in visual perception, this study recognizes the two groups of images through the widely used texture feature algorithm and support vector machine. Through the 10-fold cross validation of 185 fundus autofluorescence images, the LBP algorithm has a sound recognition effect. The 10-fold cross-validation accuracy of the 59-dimensional LBP operator with "Uniform" patterns reaches 91.89%. And in the case that the test set and the training set are randomly divided by 1:1, the recognition accuracy of 92 fundus fluorescence images in the test set reaches 88.12%, and the AUC is 0.943.