Objective To explore the influencing factors of hyperplastic scar and establish a risk prediction model. Methods From April 2021 to April 2023， 102 patients with hyperplastic scar treated in our hospital were selected as hyperplastic scar group， and 102 patients without hyperplastic scar were selected as control group. The clinical data of the two groups were compared， random forest algorithm was used to screen variables， and multivariate Logistic regression analysis was used to analyze the factors affecting the formation of hyperplastic scar. The sensitivity of the research results was analyzed by correlation E-value method. The value of β in the results of multifactor analysis was substituted into the regression equation y=1－1 / （ 1+e-z）， and the prediction model was established and its prediction efficiency was evaluated. Results A total of 9 variables were selected by random forest algorithm. Multivariate Logistic regression analysis showed that age ≤ 30 years old， scar history， family scar history， history of poor wound healing， spicy eating habits， wound type of burn， and elevated level of transforming growth factor β1（ TGF-β1） were risk factors for hyperplastic scar formation. Scar prevention measures and the increase of tumor necrosis factor-α（ TNF-α） level were protective factors. E=1.984， with a 95% CI lower limit of 1.216， the sensitivity of the research results was relatively high. When the probability of hypertrophic scar formation predicted by the model was 0.85， the Youden's J statistic was the highest （74.38）， and the prediction effect was the best. The prediction accuracy， sensitivity and specificity were 89.03%，85.73% and 88.65% respectively. The area under the ROCcurve of the subjects was 0.847（95% CI：0.782-0.913， P<0.001）， indicating good differentiation. The results of the cross over experiment showed that the fitting degree of the model parameters in the training and validation sets was high  （Nagelkerke R2=0.602）， and the model was relatively stable. Conclusion Age， scar history， dietary habits， and serum  inflammatory factor levels are related to the formation of hypertrophic scars. Personalized intervention measures should be  taken in clinical practice to reduce the rate of hypertrophic scar formation.

Hypertrophic scar, Influence factors, Prediction model, Cross over test