In order to achieve more accurate diagnosis of benign and malignant of thyroid nodule ultrasound images and avoid unnecessary puncture or biopsy surgery, a feature combining method of conventional ultrasound imaging and ultrasound elastography based on Convolutional Neural Network (CNN) was proposed to improve the accuracy of benign and malignant classification of thyroid nodules. Firstly, large-scale natural image datasets were used by the convolutional network model for pre-training, and the feature parameters were transferred to the ultrasound image domain by transfer learning to generate depth features and process small samples. Then, the depth feature maps of conventional ultrasound imaging and ultrasound elastography were combined to form a hybrid feature space. Finally, the classification task was completed in the hybrid feature space, and an end-to-end convolution network model was constructed. The experiments were carried out on 1156 images, the proposed method had the accuracy of 0.924, which was higher than that of other single data source methods. The experimental results show that, the edge and texture features of the image are shared by the shallow convolutions, the abstract features of the high-level convolutions are related to the specific classification tasks, and the transfer learning method can solve the problem of insufficient data samples. At the same time, the elastic ultrasound image can objectively quantify the lesion hardness of thyroid nodules, and with the combination of the texture contour features of conventional ultrasound image, the mixed features can more fully describe the differences between different lesions. Therefore, this method can effectively and accurately classify the thyroid nodules, reduce the pain of patients, and provide doctors with more accurate auxiliary diagnostic information.