To investigate the risk factors for gallbladder stone formation and to construct and validate a columnar line drawing model of gallbladder stone formation. Methods    854 cases of gallbladder stones examined at the Physical Examination Center of the First Hospital of Hebei Medical University from September 2022 to August 2023 were selected as the study group, and 1799 cases without gallbladder stones from the same period of physical examination were randomly selected as the control group. The Mann-Whitney U test for independent samples was used to compare the clinical data of the two groups. The study group and the control group were randomly divided into a training set and a validation set. Univariate and multivariate binary logistic regression was performed on the training set to screen the risk factors for gallbladder stone formation and construct a column-line graphical model. The accuracy and stability of the column charts were assessed using subject work characteristics (ROC) curves, calibration curves, consistency indices (C-Index), and decision curve analysis (DCA). Results    The differences in age, BMI, GLU, TG, TC, HDL-C, LDL-C, BUN, CREA, and TB between the study group and the control group were statistically significant (P<0.05). After univariate and multivariate Logistic regression analyses, three variables, age, BMI, and GLU, were selected to establish a column-line graph prediction model. The area under the curve (AUC) values for the training and validation sets were 0.731 and 0.725 respectively, the goodness-of-fits for the Hosmer-Lemeshow test were (χ2=11.78, P=0.23) and (χ2=13.43, P=0.14) respectively, and the C-Indices of the calibration curves were 0.462 and 0.450 respectively (P>0.05). Decision curve analysis shows large positive yields in the range of 1%-70% and 1%-50% for the training and validation sets, respectively. Conclusion    Age, BMI, and fasting glucose are risk factors for gallbladder stone formation, based on which we developed a columnar graphical model of the risk of gallbladder stones, with a good accuracy and stability after validation, facilitating personalized risk assessment in the clinic.