Diabetic macular edema is one of the main causes of blindness, the examination of OCT images by professional doctors is the main method to diagnose the DME, but this process is not only time-consuming but also prone to misjudgment. An auxiliary diagnostic model was proposed to discriminate the DME and normal macula. Firstly, the original OCT image is preprocessed by denoising, flattening and cropping to get the lesion area image which is easy to classify. Based on the pyramid model of wavelet decomposition, texture features are extracted from the original image and low-frequency sub-image by local binary mode method, and then fused with the gray-gradient co-occurrence matrix feature of the extracted detail image to form the final global feature, and reduce its dimension. Finally, the sequential minimal optimization algorithm of the weka platform was used to classify these images. The experimental results on Duke University and clinical datasets show that the accuracy, sensitivity and specificity of the proposed algorithm are 95.7% and 95.3%, 95.3% and 95.5% , 96.0% and 95.1% respectively. Therefore, the method can effectively classify OCT images and provide technical support for clinical auxiliary diagnosis of retinal diseases.