Objective To explore the risk factors for complications after total knee arthroplasty (TKA) and to construct a nomogram prediction model. Methods A retrospective study was conducted on 184 patients with TKA admitted to Zhengzhou Central Hospital from June 2022 to June 2025. They were divided into occurrence group and non-occurrence group according to whether postoperative complications occurred. The clinical data of the two groups were compared. Logistic regression analysis was used to analyze the factors affecting postoperative complications in TKA patients, and the nomogram and receiver operator characteristic (ROC) curve, calibration curve and decision curve analysis (DCA) were constructed for verification. Results A total of 184 patients with TKA were included in this study. According to the postoperative complications, they were divided into the occurrence group (41 cases, 22.28%) and the non-occurrence group (143 cases, 77.72%). There were significant differences in body mass index (BMI), operation time, hospitalization time, whether receiving professional functional training, and visual analogue scale (VAS) score between the two groups (all P＜0.05). After multivariate Logistic regression analysis, BMI, operation time, hospitalization time and postoperative VAS score were finally determined as risk factors for postoperative complications in TKA patients (all P＜0.05). The ROC curve showed that the area under the curve (AUC) of the the nomogram model was 0.958 (95%CI: 0.926～0.990). The calibration curve showed that the model’s predicted probabilities align well with the actual probabilities, indicating that the model has good calibration performance. The DCA results indicated that within most threshold ranges, the model curve was higher than the two extreme curves, and the clinical net benefit was relatively high. Conclusion BMI, operation time, hospitalization time and postoperative VAS score are the factors affecting postoperative complications in TKA patients. The constructed nomogram model based on these factors can be used for clinical prediction of the occurrence of postoperative complications in TKA patients, and can assist in formulating targeted intervention strategies.