Objective To construct a Nomogram model for individualized prediction of risk of pulmonary infection (PI) in patients with non-small cell lung cancer (NSCLC) during chemotherapy period, and validate the prediction efficiency of the model. Methods 218 patients with NSCLC who were treated in a hospital from February 2018 to January 2021 were selected as the research objects, according to whether they had PI after chemotherapy, they were divided into PI group (n=56) and pulmonary non-infection group (n=162), LASSO and logistic regression analysis were used to screen the independent risk factors for PI in NSCLC patients during chemotherapy, and a Nomogram prediction model was constructed. Results 218 NSCLC patients were included in study, 56 patients had PI during chemotherapy, incidence of PI was 25.69%. LASSO and logistic regression analysis showed that age ≥ 60 years old, diabetes mellitus, combined chemotherapy drugs, chemotherapy cycle >2 times, albumin content after chemotherapy < 30 g/L, KPS score < 80 points before chemotherapy were independent predictors of PI in NSCLC patients during chemotherapy (all P < 0.05). Based on 6 independent predictors, the Nomogram model was constructed to predict the risk of PI in NSCLC patients during chemotherapy. Validation result showed that the C -index of the training set and validation set were 0.819 (95%CI: 0.788-0.850) and 0.802 (95%CI: 0.778-0.829) respectively. The calibration curve trend of two sets was relatively close to the diagonal (ideal curve), the area under the ROC curve (AUC) were 0.807 (95%CI: 0.775-0.839) and 0.797 (95%CI: 0.773-0.821) respectively. When the decision curve showed that the threshold probability was 1%-90%, there was a relatively high net profit value. Conclusion Nomogram model based on independent predictors of PI in NSCLC patients du-ring chemotherapy has good prediction efficiency, which is helpful to screen high-risk patients as early as possible and improvement of treatment plans.