Abstract: Breast cancers are the most common malignant tumors in women, and how to use ultrasound to diagnose breast cancers quantitatively is still an unsolved problem. This paper extracts five elastic features based on the elastography images of the breast tumors, furthers extract four features related to gray co-occurrence matrix to describe the texture of breast masses. we study the application of SVM classifier to classify these features, and uses the consistency, classification accuracy, ROC curve and AUC (area under the curve) to assess the classification results. we used ultrasound imaging technique to collect data from the actual patients, with the data of 195 lesions in 142 patients. Experimental results show that the classification performance of the elastic features is good, and the support vector machine is suitable for breast image classification, and its classification accuracy is high, which provides a good value for diagnosis. Meanwhile, it is found that the extracted features related to gray level co-occurrence matrix have a low diagnostic value.