Venipuncture is one of the common operations used by doctors in the outpatient blood drawing room. The success rate of puncture is not only related to the pain degree of the patient, but also affects the test results. In this study, we propose an ultrasound guided venipuncture vascular recognition system based on deep learning. First, kmeans++ clustering is performed for the vascular regions in the different B-mode ultrasound images to facilitate estimation in subsequent work. Second, a lightweight vascular ultrasound network (UV-YOLOv7) is designed, specifically, based on YOLOv7-tiny, a multi-scale feature fusion module (MFFM) is designed to better fuse the high-level semantic features and low-level detail features, and the speed and accuracy of model detection are enhanced by lightweighting the model structure and replacing the EIoU loss function. Finally, a Dynamic Neighborhood-Density Based Spatial Clustering of Applications with Noise (DN-DBSCAN) algorithm is proposed, which can cluster a series of local vascular regions using the localization results and confidence properties of the network output to remove the misdetected regions. In the experiment, We selected 303 artifact-free and 264 heavily artifacted vascular ultrasound images for offline expansion and trained on the experimental platform, The results show that the proposed method performed best with an mAP of 86.2% and an inference time of 0.6 ms. At the end of the experiment, more robust vascular localization results were obtained by DN-DBSCAN clustering. [ABSTRACT FROM AUTHOR]