This study aimed to establish a predictive model of severe adenovirus pneumonia in children based on low-dose CT imaging and clinical features of the chest and evaluate the model.This retrospective study included 177 pediatric adenovirus patients who underwent low-dose CT scans between January 2019 and August 2019. We collected clinical and imaging manifestations, complications, and laboratory test indicators in two groups of children and conducted all statistical analyses. Using a logistic regression model to analyze severe adenovirus pneumonia risk factors in children. We constructed a prediction model by drawing a nomogram and verified the predictive efficacy of the model through the ROC curve.The difference was statistically significant (P<0.05) between the mild adenovirus pneumonia group and the severe adenovirus pneumonia group in gender, age, weight, body temperature, L/N ratio, LDH, ALT, AST, CK-MB, adenovirus DNA loads, bronchial inflation sign, emphysema, ground glass sign, bronchial wall thickening, bronchiectasis, pleural effusion, consolidation score, and lobular inflammation score. Multivariate logistic regression analysis showed that gender, LDH value, emphysema, consolidation score, and lobular inflammation score were severe independent risk factors for adenovirus pneumonia in children. Logistic regression was employed to construct clinical models, imaging semantic feature models, and combined models. The clinical decision curve analysis demonstrates the clinical application value of the nomogram prediction model.The comprehensive nomogram prediction model, based on low-dose chest CT imaging and clinical data, can predict the high-risk factors of severe adenovirus pneumonia in children and has good clinical application value.