Background and Aims: The incidence of complications after hepatectomy is relatively high, and early screening of high-risk population for severe complications after hepatectomy is of great significance to reduce the incidence of severe complications after surgery. This study was conducted to analyze the risk factors for severe postoperative complications in patients undergoing surgery for hepatocellular carcinoma (HCC) and establish an individualized nomogram risk prediction model for severe postoperative complications, so as to provide a reference for the optimization of the perioperative management of HCC patients.  
Methods: The data of HCC 854 patients who underwent hepatectomy from January 2018 to June 2020 in the Department of Hepatobiliary Surgery of the First Affiliated Hospital of Guangxi Medical University were retrospectively analyzed. The risk factors for severe postoperative complications were screened by univariate analysis and Logistic regression analysis, and then a nomogram risk prediction model was established. The internal validation of the model was assessed using the Bootstrap method, and the discrimination and calibration aspects of the model were assessed by ROC curves and calibration plots. 
Results: Among the 854 patients, serious postoperative complications (≥ grade III) occurred in 86 cases (10.1%). The results of univariate analysis and Logistic regression analysis revealed that liver cirrhosis (OR=1.905, 95% CI=1.153–3.147, P=0.012), surgical procedure (OR=3.412, 95% CI=1.618–7.192, P=0.001), intraoperative plasma transfusion (OR=2.518, 95% CI=1.51–4.199, P<0.001), operative time (OR=1.003, 95% CI=1.002–1.005, P<0.001), postoperative albumin level (OR=0.922, 95% CI=0.873–0.973, P=0.003) and postoperative aspartate transaminase level (OR=1.001, 95% CI=1.000–1.002, P=0.006) were independent influencing factors for severe complications in HCC patients after hepatectomy. The C-index of the nomogram model to predict the risk of severe postoperative complications was 0.774, and the area under the ROC curve for the nomogram model was 0.788 (95% CI=0.74–0.836). 
Conclusion: The established individualized nomogram risk prediction model for severe complications after hepatectomy based on 6 clinical factors has good predictive performance. This model can be used for early identification of high-risk patients and provide a basis for medical staff to take preventive measures.