Objective To explore the risk factors for the occurrence of stroke-related infection in patients with acute ischemic stroke (AIS), and construct a decision tree prediction model. Methods AIS patients admitted to the department of neurology of a hospital from June 2020 to June 2021 were retrospectively selected as the research objects. They were divided into training group and validation group in a certain proportion. The predictors were screened by Lasso regression, and a decision tree model for stroke-related infection in AIS patients was constructed based on the CHAID algorithm. Random split validation method was adopted for internal validation, and the area under the receiver operating characteristic (ROC) curve (AUC) was used to evaluate the effect of the model. Results A total of 693 AIS patients were treated, 484 in training group and 209 in validation group. Incidence of stroke-related infection in training group and validation group were 17.8% (n=86) and 20.1% (n=42) respectively. Age, fasting blood glucose, history of diabetes, triglycerides, smoking, complicated respiratory diseases, complicated cardiovascular diseases, disturbance of consciousness, and long length of hospitalization were risk factors for stroke-related infection in AIS patients. The above factors were included and a decision tree model was constructed. The decision tree model contained 3 layers and a total of 7 nodes. Complicated respiratory disease, history of diabetes, and smoking were predictors of stroke-related infection. The AUC of ROC of validation group decision tree model was 0.980, the sensitivity and specificity were 97.0% and 97.6% respectively, Youden index was 0.946, Kappa value was 0.914. Conclusion The model constructed in this study can better predict the risk of stroke-related infection in AIS patients, and can be used as an evaluation tool for clinical nurses to predict the risk of patients.