〔Abstract〕 Objective To explore the clinical application value of deep convoluti onal neural network for automatic detection and classification of benign and malignant thyroid nodules ultrasound images. Methods A total of 1 012 ultrasound images of thyroid nodules were retrospectively selected and labeled.The YOLOv5 network model was constructed to accurately locate the location of thyroid nodules and automatically trim the area of the nodules.At the same time, a GoogLeNet network model was constructed to classify benign and malignant nodules after reduction. Results In the collected data set, the mean accuracy of the target detection network for thyroid nodule location detection was 96.2%.Meanwhile, the sensitivity, specificity, accuracy and AUC of the classification network for benign and malignant nodules were 0.885,0.822,0.866 and 0.92 respectively, which were significantly higher than those of the AlexNet model(0.81),VGG model(0.86) and MobileNet model(0.76). Conclusion The deep convolutional neural network model has high localization and recognition ability for benign and malignant thyroid nodules in ultrasound images, which is helpful to improve the accuracy of automatic image diagnosis.