In recent years, there has been an increase in the number of people diagnosed with thyroid cancer. Thyroid cancer mortality can be considerably reduced by early detection of thyroid nodules. Ultrasound is usually the first choice for thyroid imaging. This paper systematically summarizes the thyroid nodule diagnosis algorithm of convolutional neural network (CNN) for ultrasonic images based on the relevant literature published at home and abroad in recent years. The main content includes the application of CNN in the three aspects of thyroid nodule region extraction, benign and malignant classification, and calcification recognition. To provide a clearer reference to researchers, the basic design idea, network architecture form, related improvement purpose, and method of each algorithm are described. Finally, the algorithms for thyroid nodule diagnosis based on CNN are summarized and analyzed, and future research hotspots and related challenges are discussed.