〔Abstract〕 Objective To construct and appraise a new model for predicting the prognosis of rectal cancer patients using the Lasso-Cox strategy. Methods The clinical pathological data of 599 rectal cancer patients who underwent radical resection were analyzed. Comparison between groups, Lasso and Cox regression were used to select variables and construct a model, and its discrimination, consistency, and clinical benefits were appraised by the receiver operating characteristic(ROC), calibration curve, and decision curve analysis. Results Comparison between groups showed that age, body mass index(BMI), preoperational nutrition status, carbohydrate antigen199(CA199), preoperative chemotherapy, intraoperative blood transfusion, vascular or nerve invasion, cancer nodules, pathologic T, N, and TNM stages, tumor recurrence or metastasis, radiotherapy and postoperative survival time were associated with grouping of death or survival in rectal cancer patients. Among them, 8 variables were selected by lasso and contained into the Cox regression model. Age(HR=1.04,P<0.05), BMI(HR=0.89,P<0.05), blood transfusion(HR=2.29,P<0.05), postoperative chemotherapy(HR=0.16,P<0.01), recurrence(HR=43.67,P<0.01), and metastasis(HR=2.75,P<0.05) were identified as independent prognostic factors, which were used to construct a nomogram model. The area under the curve(AUC) and the 95% confidence interval of the receiver operating characteristic(ROC) curve of the predictive model was 0.95(0.91-0.99),P<0.01. The predicted probability of 1-year and 3-year survival was close to the actual probability. The DCA curve of the model was far away from a decision line parallel to the X-axis and another line with a negative slope. Conclusion The newly established nomogram has good discrimination, consistency and clinical benefits, which help predict the prognosis of rectal cancer after surgery.