Objective To analyze the risk factors for postoperative intra-abdominal infection IAI in gastrointestinal cancer GIC patients then construct and validate the nomogram model . Method A total of 230 GIC patients who admitted to our hospital from January 2018 to December 2023 were selected as the study group. According to a ratio of 7 to 3 patients were randomly divided into a modeling group of 161 cases and a validation group of 69 cases. Based on modeling group data Lasso-logistic regression was used to analyze the risk factors of postoperative IAI in GIC then construct and validate the nomogram model. Result Lasso regression screened five non - zero coefficient indicators which were diabetes ASA grade > grade Ⅱ combined organ resection operation time and preoperative CONUT score. Multivariate logistic regression analysis showed that ASA grade >Ⅱ combined organ resection surgery time≥4. 5 hours and preoperative CONUT score≥5 were independent risk factors for postoperative IAI in GIC patients all P<0. 05 . Based on the above indicators a nomogram model was constructed. The ROC curve showed that the AUC for predicting IAI in the modeling group and validation group patients were 0. 829 95% CI = 0. 759 - 0. 899 and 0. 862 95% CI = 0. 725 - 0. 999 . The calibration curve indicated that the model had good consistency between the predicted probability and the actual probability in the modeling group and validation group. The decision curve indicated that the model had a wide range of clinical net benefits in the modeling and validation groups. Conclusion The nomogram developed in this study can effectively identify high-risk postoperative IAI patients in GIC and can be used to guide clinical practice.