Background and Aims Lower extremity deep venous thrombosis (LDVT) is a common postoperative complication in patients with general surgical malignancies, significantly affecting their postoperative recovery. Currently, assessment tools cannot provide a detailed risk stratification for LDVT patients. Therefore, this study aims to explore the influencing factors for LDVT occurrence in patients with general surgical malignancies after surgery and establish a reliable prediction tool to assist in diagnosing and preventing LDVT.Methods The clinical data of patients undergoing inpatient surgery for malignant tumors in the Department of General Surgery, Xiangya Hospital, Central South University from January 1, 2021, to October 31, 2022, were retrospectively collected, and the cases were strictly quality-controlled according to well-designed inclusion and exclusion criteria. Established LDVT influencing factors and important clinical features were used as analysis variables. Univariate and multivariate analyses were performed to evaluate the influencing factors for LDVT and screen predictive factors for the model. A receiver operating characteristic (ROC) curve based on Logistic regression was created using programming software to assess the model's predictive performance. A calibration curve was used to evaluate the goodness of fit between the prediction model and the data. Decision curve analysis was employed to compare the clinical application value of the prediction model with other single indicators.Results A total of 342 patients were included, with 167 cases in the LDVT group and 175 cases in the control group. Univariate analysis revealed that a history of surgical trauma within one month, hypertension, smoking, alcohol consumption, history of radiotherapy, duration of ICU stay, red blood cell (RBC) count, hemoglobin (Hb) level, fibrinogen degradation products (FDP), D-dimer, coagulation time, surgical duration, intraoperative RBC transfusion, intraoperative plasma transfusion, and surgical approach were all related to the occurrence of LDVT (all P<0.05). Multivariate analysis demonstrated that a history of surgical trauma within one month, FDP, coagulation time, surgical duration, intraoperative RBC transfusion, and intraoperative plasma transfusion were independent influencing factors for postoperative LDVT (all P<0.05). A nomogram was constructed by using these independent influencing factors as predictor variables, and the area under the ROC curve (AUC) for predicting LDVT risk at 2 weeks after surgery was 0.830 (95% CI=0.787-0.874, P<0.001). The Hosmer-Lemeshow statistic in the calibration curve was 0.973. Decision curve analysis demonstrated that the model had a better net benefit than single indicators.Conclusion The prediction model developed in this study exhibits good discriminative ability and clinical application value. It can assist clinicians in risk stratification for LDVT in high-risk populations and facilitate the attainment of personalized and effective prevention and treatment measures. Future studies should focus on testing and improving the external validity of the model through multicenter, prospective research designs incorporating intelligent algorithms.