Abstract: Objective To construct a new nomogram model based on clinical indicators to distinguish between active pulmonary tuberculosis (APTB) and inactive pulmonary tuberculosis (IPTB). Method: A total of 622 patients diagnosed with tuberculosis in the Sixth People's Hospital of Nantong from January 2019 to December 2022 were retrospectively summarized, and were randomly divided into 421 patients in the training set and 201 patients in the verification set by 2:1. The clinical data and blood biochemical indexes of APTB and IPTB patients in the training set were compared, the predictive factors of APTB were screened by multivariate Logistic regression analysis, and the R software was used to establish a column graph. Result There was no significant difference between 271 (64.4%) and 116 (57.7%) patients diagnosed with APTB in the training set and the verification set (χ2=2.566, P=0.109). Compared with APTB and IPTB patients, APTB has typical symptoms and an increased number of diabetic cases,white blood cell count (WBC), percentage of neutrophils, platelet count (PLT), platelet-lymphocyte ratio (PLR), neutrophil to lymphocyte ratio (NLR), highly sensitive C-reactive protein (hs-CRP), hs-CRP to albumin ratio (HSCAR), hs-CRP to prealbumin ratio (HSCPR) The ratio of hs-CRP to lymphocyte (HSCLR), erythrocyte sedimentation rate (ESR), monocyte to high-density lipoprotein (MHR), procalcitonin (PCT), lactate dehydrogenase (LDH), alkaline phosphatase (ALP) and adenosine deaminase (ADA) were significantly increased. Mean erythrocyte volume (MCV), erythrocyte specific volume (HCT), hemoglobin (Hb), mean erythrocyte hemoglobin concentration (MCHC), mean platelet volume (MPV), platelet volume distribution width (PDW), urea nitrogen, blood sodium, LDH to adenylate dehydrogenase ratio (LAR) and albumin were significantly decreased (P＜0.05). MCV, ESR, albumin, ADA, MHR and HSCLR were independent predictors of APTB (P＜0.05). The area under the curve (AUC) of the receiver operating curve (ROC) calculation training set and verification set were 0.956 and 0.876, respectively, indicating good model differentiation. The calibration curve and decision curve showed a good agreement between the model and the clinical net benefit ratio. A scoring system was developed based on the nomogram. The AUC calculated by ROC was 0.899, the optimal critical value was 15 points, the sensitivity was 85.6%, and the specificity was 93.7%. Conclusion Using the most readily available blood biochemical indexes to construct the column chart scoring model has good application value to guide the clinical differential diagnosis of APTB and IPTB.