Aim To investigate the risk factors of restenosis after stent implantation in patients with vertebral artery stenosis and construct the prediction model of nomogram. Methods The clinical data of 272 patients with digital subtraction angiography (DSA) confirmed vertebral artery stenosis and stent implantation admitted to the First Affiliated Hospital of Xinjiang Medical University from January 2016 to June 2023 were collected and retrospectively studied. According to the time of stent implantation, 272 patients were divided into modeling group (from January 2016 to December 1,0 cases) and verification group (from January 2022 to June 3,2 cases). In the modeling group, patients were divided into in-stent restenosis (ISR) group (50 cases) and non-ISR group (170 cases) according to CT angiography (CTA) or DSA results. Based on the independent risk factors of ISR analyzed by LASSO regression and multiple Logistic regression, a nomogram prediction model was constructed. The predictive ability of the prediction model was evaluated by the area under the receiver operating characteristic curve (AUC). The clinical application value of the prediction model was evaluated by clinical decision curve. Results In the modeling group, ISR occurred in 50 of 220 patients undergoing vertebral artery stenting, and the incidence of ISR was 22.72%. LASSO regression and multivariate Logistic regression analysis suggested that high ESSEN stroke risk score(ESRS), hyperhomocysteinemia (HHcy), moderate or higher stenosis of internal carotid artery and/or contralateral vertebral artery, low density lipoprotein cholesterol (LDLC) ≥1.8 mmol/L, low postoperative peak systolic velocity (PSV) of vertebral artery and small stent diameter were risk factors for ISR. A nomogram prediction model was built based on the above six variable factors. The nomogram predicted that the AUC of ISR after vertebral artery stenting was 0.857(95%CI:0.799～0.915) in the modeling group, and the AUC of the verification group was 0.847(95%CI:0.732～0.961), which suggested that the model had a good degree of differentiation. Conclusion The prediction model established in this study can better predict the risk degree of ISR in patients with metal stent implantation of vertebral artery, which is helpful for clinicians to find high-risk patients with ISR and make timely intervention, so that patients can get greater benefits.