With the rapid advancement of medical imaging technology and the rapid development in artificial intelligence, intelligent medicine has emerged as a prominent focus of medical study. Although ultrasound imaging technology has many therapeutic uses, most vascular extraction techniques are manual or semiautomatic, and the extraction results are highly subjective and error-prone. For preprocessing carotid artery features, this work uses a multiscale Hessian filtering synergistic technique. It then uses medical prior knowledge to extract the region of interest (ROI) of blood vessels, creates a traversal tracking search algorithm to find blood vessels, and automatically extracts the carotid artery vessel wall using pixel grayscale difference grading. The extraction accuracy can reach 89.3%. This study can lessen the load on physicians, reduce the rate of misdiagnosis owing to subjective diagnosis and allow physicians to perform a quantitative and qualitative examination of vascular morphological features, making clinical diagnosis more objective and accurate.