〔Abstract〕 Abstract Objective To construct and validate a clinical diagnostic model to differentiate between pulmonary tu- berculosis and non-tuberculous lung diseases . Methods Information was collected from 258 patients with respirato- ry system diseases , and they were divided into a training set of 152 cases and a test set of 106 cases with a ratio of 6:4 using the random number seed method in R software . The training set was further divided into a tuberculosis group of 95 cases and a non-tuberculosis group of 57 cases , and the test set into a tuberculosis group of 65 cases and a non-tuberculosis group of 41 cases based on the diagnosis of pulmonary tuberculosis . A diagnostic model was constructed using multivariate logistic regression analysis to determine the influencing factors of pulmonary tubercu- losis . The diagnostic value and clinical utility of the model were assessed using the Receiver Operating Characteris- tic (ROC) curve , calibration curve , and Decision Curve Analysis (DCA) . Results CD161 + % ( OR = 0. 768 ; 95% CI 0. 697 - 0. 845 ; P < 0. 001) , AST (OR = 0. 961 ; 95% CI 0. 930 - 0. 993 ; P = 0. 019) , and smoking his- tory (OR = 3 . 181 ; 95% CI 1 . 149 - 8. 804 ; P = 0. 026) were identified as independent risk factors for the occur- rence of pulmonary tuberculosis . In both the training and test sets , the area under the ROC curve (AUC) reached 0. 870 (95% CI 0. 816 - 0. 924) and 0. 887 (95% CI 0. 827 - 0. 948) , respectively . The Hosmer-Lemeshow good- ness-of-fit test showed a good fit ( training set χ2 = 6. 213 , P = 0. 623 ; test set χ2 = 6. 197 , P = 0. 625) . DCA indi- cated that the model had good reference significance for the diagnosis of the probability of pulmonary tuberculosis occurrence . Conclusion The diagnostic model constructed using the percentage of CD161 + % , AST levels , and smoking history has certain diagnostic performance , facilitating rapid clinical differentiation between pulmonary tu- berculosis and non-tuberculous lung diseases .