Background: The aim of this study was to develop and internally validate a postoperative NVG risk nomogram in a Chinese population of patients with PDR.Methods: We developed a prediction model based on a training dataset of 107 PDR patients who underwent vitrectomy from March,2017 to March,2018 in Shenyang Fourth People’s Hospital, and they were followed up for at least 12 months. The presence or absence of NVG were observed. The least absolute shrinkage and selection operator regression model was used to optimize feature selection for the postoperative NVG risk model. Multivariable logistic regression analysis was applied to build a predicting model incorporating the feature selected in the least absolute shrinkage and selection operator regression model. Discrimination, calibration, and clinical usefulness of the predicting model were assessed using the C-index, calibration plot, and decision curve analysis. Internal validation was assessed using the bootstrapping validation.Results: Predictors contained in the prediction nomogram included HbAlc level, presence of diabetic nephropathy and anti-VEGF therapy. The model displayed good discrimination with a C-index of 0.852 (95% CI: 0.740–0.964) and good calibration. High C-index value of 0.849 could still be reached in the interval validation. Decision curve analysis showed that the NVG nomogram was clinically useful when intervention was decided at the NVG possibility threshold of 2%.Conclusion: This novel NVG nomogram incorporating HbAlc level, presence of diabetic nephropathy and anti-VEGF therapy could be conveniently used to facilitate the postoperative NVG risk prediction in PDR patients.