Objective To explore the relevant factors for infection in patients with non-small cell lung cancer (NSCLC) during chemotherapy period, and construct a prediction model. Methods 387 NSCLC chemotherapy patients who admitted to a hospital from May 2020 to January 2023 were selected as the modeling set. They were divided into an infection group and a control group based on the occurrence of infection during chemotherapy period. General data, diagnosis and treatment information, and biochemical examination results of patients were collected for univariate analysis. Items with statistically significant differences were included in logistic regression analysis, and factors related to infection were screened out. A infection risk prediction model for NSCLC chemotherapy patients was constructed using the RMS package in R-based software. The discrimination and consistency of the model in infection prediction were validated through the area under the curve (AUC) of the receiver operating characteristic (ROC) curve and the calibration curve. Clinical benefits were evaluated through the decision curve analysis (DCA). A total of 165 NSCLC chemotherapy patients who admitted to the hospital from February 2023 to October 2024 were included as the validation set, and external validation of the model was conducted. Results Among the 387 patients in the modeling set, 93 cases developed infection during chemotherapy period, with an infection rate of 24.03%. The main infection sites were respiratory system (39.79%) and digestive system (24.73%). There were statistically significant differences in age, combined chronic respiratory disease, tumor-node-metastasis-based (TNM) staging system, chemotherapy cycle, combined radiotherapy, episode number of invasive procedures, and glucocorticoid use between two groups of patients in the modeling set (all P<0.05). Logistic regression analysis showed 6 major risk factors for co-infection in NSCLC chemotherapy patients, namely age, TNM stage Ⅲ-Ⅳ, long chemotherapy cycle, combined radiotherapy, more than 2 episodes of invasive procedures, and glucocorticoid use. A risk prediction model for the infection in NSCLC chemotherapy patients was constructed using the 6 predictive indicators. The results showed that the AUCs of the modeling set and the validation set were 0.792 and 0.773, respectively. The predicted probability of infection was close to the actual probability. The HL test of goodness of fit of the modeling set showed χ² value of 8.760 and P value of 0.316, and those of the modeling set were 9.013 and 0.287, respectively. DCA revealed a high clinical benefit of the model. Conclusion A nomogram prediction model based on age, TNM stage, chemotherapy cycle, combined radiotherapy, episodes of invasive procedures, and glucocorticoid use can effectively predict co-infection in NSCLC chemotherapy patients.