Objective To identify risk factors for colorectal adenomatous polyps using logistic regression analysis， construct a prediction model based on these identified factors， and subsequently evaluate the performance of the model. Methods Encompassed 1，023 patients who underwent large intestine polyp resection at the First Hospital of Handan between January 2017 and January 2022. Among these patients， 676 had adenomatous polyps （adenomatous polyp group） and 347 had non-adenomatous polyps （non-adenomatous polyp group）. We collected data on basic information， medical history， colonoscopy results， and polyp pathology. By comparing the two groups， we identified significant differences in various indicators， which were selected as candidate factors for model construction. Patients were randomly divided into a training set and a validation set at an 8∶2 ratio. Using the training set data， we constructed a risk prediction model and developed a nomogram using R Studio software to visually present the model. Finally， we internally validated the model using the validation set. The model's discrimination ability was evaluated using the ROC curve， its accuracy was assessed via the calibration curve， and its clinical utility was evaluated through decision curve analysis （DCA）. Results Significant differences were observed between the two groups in terms of age， drinking habits， family history of colorectal cancer， hyperlipidemia， history of cholecystectomy， HP infection， and history of appendectomy （P < 0.05）. These variables were included in the model construction. A total of 818 participants were randomly assigned to the training set， while 205 were allocated to the validation set. Multivariate logistic regression analysis on the training set confirmed that age （OR = 1.021， 95%CI： 1.006 ~ 1.036， P = 0.006）， alcohol consumption （OR = 3.440， 95%CI： 2.251 ~ 5.257， P < 0.001）， first-degree relatives with colorectal cancer （OR = 3.775， 95%CI： 1.881 ~ 7.577， P < 0.001）， hyperlipidemia （OR = 3.428， 95%CI： 2.443 ~ 4.808， P < 0.001）， history of cholecystectomy （OR = 3.916， 95%CI： 1.756 ~ 8.735， P < 0.001）， Helicobacter pylori （HP） infection （OR = 3.292， 95%CI： 2.309 ~ 4.693， P < 0.001）， and history of appendectomy （OR = 3.819， 95%CI： 2.002 ~ 7.286， P < 0.001） were independent risk factors for adenomatous polyps. Consequently， a prediction model for large intestine adenomatous polyps was developed using the formula P = 1/（1+e^-Y）， where Y = 0.020 × age + 1.328 × first-degree relatives with colorectal cancer + 1.235 × alcohol consumption + 1.232 × hyperlipidemia + 1.365 × cholecystectomy + 1.192 × HP infection + 1.340 × appendectomy - 1.995. The model demonstrated good performance with AUC values of 0.763 （95%CI： 0.729 ~ 0.797） for the training set and 0.769 （95%CI： 0.644 ~ 0.787） for the validation set. The calibration curve indicated a good fit， and decision curve analysis showed that the model could achieve positive net benefit across a wide range of threshold probabilities， confirming its clinical utility. Conclusions Age， alcohol consumption， a family history of colorectal cancer in first-degree relatives， hyperlipidemia， cholecystectomy， HP infection， and appendectomy were identified as independent risk factors for adenomatous polyps. A prediction model incorporating these risk factors holds significant practical value for predicting the occurrence of colorectal adenomatous polyps.