Objective To analyse the risk factors for postoperative infection in traumatic tibial plateau fractures and to construct a Nomogram prediction model.Methods One hundred and forty-eight patients with traumatic tibial plateau fractures who underwent surgery in our hospital from October 2019 to August 2021 were selected for the study,and were divided into an infected group(n=20)and an uninfected group(n=128)according to whether they developed infection after surgery.The general data of the two groups were compared;the predictive value of statistically significant continuous variables was analysed using the ROC experiment;the risk factors affecting postoperative infection in traumatic tibial plateau fractures were analysed using the logistic regression experiment;and the clinical efficacy of the Nomogram model was verified using internal data.Results In the comparison of general data such as age and gender between the two groups,the differences were not statistically significant(P＞0.05).Compared with the uninfected group,patients in the infected group had a higher percentage of diabetes mellitus,open fracture type,and osteofascial compartment syndrome,and longer operative time and hospital stay(P＜0.05);diabetes(yes),fracture type(open),osteofascial compartment syndrome(yes),and operative time(＞3 h)were risk factors affecting postoperative infection in traumatic tibial plateau fractures.The AUCs for operative time and hospital stay were not 0.792 and 0.651;the optimal stage values were not 3 h and 13 d(P＜0.05);the Nomogram model predicted a C-index of 0.744(0.651-0.807)for the risk of postoperative infection in traumatic tibial plateau fractures.The model predicted the risk of infection after traumatic tibial plateau fracture at a threshold of＞0.09.Conclusion Diabetes mellitus(yes),fracture type(open),osteofascial compartment syndrome(yes),and operative time(＞3 h)were risk factors affecting postoperative infection in traumatic tibial plateau fractures,and the Nomogram model constructed based on the above variables had good predictive value.