Cardiovascular disease is a common disease that poses a serious threat to humanity, with a high incidence, disability, and mortality rate that is daunting. Carotid Atherosclerosis (CAS) is closely related to cardiovascular disease, so it is very important to accurately identify carotid plaque in ultrasound images for the diagnosis of stroke risk patients. At present, traditional ultrasound testing has strong subjectivity, poor reproducibility, and there are cases of missed detections. To address these issues, this article proposes an improved carotid artery ultrasound plaque recognition and localization algorithm. Firstly, preprocess the ultrasound image to reduce the impact of noise and shadows; Secondly, by improving the YOLOV5 model, setting custom anchor frames, and adding attention mechanisms, the final accuracy achieved was 3.48% higher than the original YOLOV5. The experimental results showed that the improved YOLOV5 model improved the recognition performance of the experimental dataset and effectively improved the network's ability to recognize the intima. Even in the case of small intra vascular differences and unclear neck boundaries, this provides new ideas for the research and application of medical image recognition.