The invention discloses a medical image lesion area positioning and classification method. The method comprises the following steps: the step 1, acquiring medical images, and dividing the images into a training set and a test set; the step 2, extracting WLD histogram information from the images of the training set, marking characteristic points of the training set according to the histogram information, placing the marked characteristic points into a KNN classifier, training the classifier, testing the trained KNN classifier using the images of the test set, and completing positioning of a medical image lesion area; the step 3, segmenting the images after the positioning through adoption of a histogram threshold method, and reserving the lesion area; and the step 4, putting the segmented images into a CNN depth model to extract characteristics, performing lesion area characteristic classification using an SVM classifier, and outputting a classification result. When the lesion area occupies a small part of the whole image, the accuracy rate is substantially improved.