Objective To analyze the application value of radiomics in the diagnosis of different pathological subtypes of chronic rhinosinusitis (CRS). Methods Clinical data of CRS patients who underwent endoscopic sinus surgery at the First Affiliated Hospital of Kunming Medical University from February 2023 to September 2024 were retrospectively analyzed. After screening according to the inclusion and exclusion criteria, a total of 143 cases were included. CT imaging data of the disease patients were collected. The cases were randomly divided into a training set (n=100) and a validation set (n=43). After extracting deep features of CT images through radiomics technology, the least absolute shrinkage and selection operator (LASSO) algorithm was used to screen the features most likely to predict eosinophilic chronic rhinosinusitis (eCRS). Combined with clinical data, single-factor and multi-factor logistic regression algorithms were used to establish radiomics model, clinical model, and combined model. Finally, the clinical application value and predictive efficacy of the models were evaluated through calibration curve analysis (CCA) and decision curve analysis (DCA). Results The area under curve (AUC) of the radiomics model in the training set and validation set was 0.96 and 0.80, respectively, while the AUC of the clinical model was 0.93 and 0.77, respectively. The predictive effect of the radiomics model was superior to that of the clinical model (Delong test, P<0.001). Notably, the combined model, which integrated clinical data, performed the best, with the AUC increasing to 0.98 and 0.86 in the training set and validation set, respectively. The evaluation results of CCA and DCA confirmed that these models had good clinical application value. Conclusions The CT radiomics-based model, particularly the combined model integrating clinical data, enables precise subtyping of CRS. This method provides a novel non-invasive assessment approach for CRS patients before surgery, helps to judge the prognosis of patients, and provides a basis for formulating personalized and precise treatment plans.