Objective To construct a risk prediction model of pulmonary involvement based on chest CT and clinical feature in patients with primary Sjogren′s syndrome （pSS）， and to explore the risk prediction value of the model. Methods A total of 360 pSS patients who had been treated at Handan Hospital of Traditional Chinese Medicine from October 2020 to August 2023 were retrospectively selected as study objects， and were then divided into a modeling group （252 patients） and a verification group （108 patients） according to a ratio of 7∶3. The patients in the modeling group were divided into a control group （201 patients） and an involvement group （51 patients） based on presence or absence of lung involvement. The data on clinical characteristics and features of chest high?resolution CT （HRCT） in the modeling group was collected. Univariate analysis was performed among the groups to determine the relevant factors affecting lung involvement in pSS patients. Binary logistic regression analysis was performed on related factors to screen independent risk factors. A prediction model was established based on the independent risk factors. A verification and value analysis of the column?line prediction model were completed through data collection of the verification group. Results Age， disease course， cough， Raynaud′s phenomenon， C?reactive protein （CRP）， anti?SSA antibody， and HRCT were the relevant factors affecting lung involvement in pSS patients （all P < 0.05）. Further binary logistic regression analysis showed that old age， prolonged disease course， cough and abnormal HRCT imaging were independent risk factors for lung involvement in SS patients （all P < 0.05）. A nomogram risk prediction model was constructed based on independent factors. The model verification results indicated that the calibration chart showed better performance in the prediction model. The AUC of the area under the receiver operating characteristic （ROC） curve was 0.993 the modeling group and 0.995 in the validation group. Conclusions The clinical characteristics and the results of chest CT are closely related with lung involvement in patients with pSS. Old age， prolonged disease course， cough， and abnormal HRCT imaging are independent risk factors affecting lung involvement in patients with pSS. The prediction model established on this basis has a higher predictive value for the occurrence of lung involvement in patients receiving after?loading radiotherapy.