Aim To construct a nomogram model for predicting the risk of coronary artery calcification in patients with chronic obstructive pulmonary disease (COPD) and evaluate its predictive efficiency. Methods The data of DRYAD database were analyzed by the R software. Multivariate Logistic regression analysis was used to screen independent predictors of coronary artery calcification risk in patients with COPD, and a personalized nomogram prediction model was constructed. Receiver operating characteristic (ROC) curve was used to evaluate the predictive effect of the nomogram model. Results Multivariate Logistic regression analysis showed that male, advanced age, use of statins and use of ACE inhibitors or ARB antihypertensive drugs were independent risk factors for coronary artery calcification in COPD patients (P＜0.05). The area under the ROC curve (AUC) of the constructed nomogram model was 0.9,5%CI: 0.725～0.834, suggesting that the nomogram had good discrimination. The Hosmer-Lemeshow goodness-of-fit test showed that there was no significant difference between the predicted probability of nomogram and the actual frequency of coronary artery calcification (χ²＝6.240, P＝0.621), that is, the nomogram model had good calibration. The DCA curve showed that when the threshold probability of coronary artery calcification in COPD patients was between 0.26 and 0.96, the net benefit of patients using the nomogram model was significantly better than that of the “full intervention” and “no intervention” measures, suggesting that the nomogram model has good clinical applicability. Conclusion The nomogram prediction model constructed in this study can be used to assist clinical staff to screen out COPD populations with high risk of coronary calcification, formulate individualized targeted intervention plans, and reduce the incidence of coronary calcification in COPD patients.