Objective To analyze the etiological characteristics, pathogenic bacteria detection and related risk factors in patients with acute cerebral infarction (ACI) complicated with bacterial pulmonary infection. Methods 178 patients with ACI treated in our hospital from July 2020 to July 2023 were selected as the research objects, they were divided into bacterial lung infections (n=45) and non-bacterial lung infections (n=133). The aetiological characteristics and the detection of pathogenic bacteria in ACI patients were analyzed, and the risk factors affecting ACI patients with bacterial lung infection were analyzed by Logistic regression.Results 45 patients with bacterial pulmonary infection, 58 strains of pathogenic bacteria were cultured, mainly gram-negative bacteria, accounting for 76.50%, among which Klebsiella pneumoniae and pseudomonas aeruginosa were dominant, accounting for 25.86% and 18.96%, respectively. The age, invasive operation, complete bed rest, NIHSS score at admission and the rate of massive cerebral infarction in the bacterial pulmonary infection group were higher than those in the non bacterial pulmonary infection group (P＜0.05). Logistic multivariate regression analysis showed that age (OR=1.510, 95%CI:1.229-1.855), invasive surgery (OR=1.496, 95%CI:1.201-1.864), complete bed rest (OR=1.471, 95%CI: 1.205-1.797), NIHSS score at admission (OR=1.456, 95%CI:1.465-1.821) and large cerebral infarction (OR=1.508, 95%CI: 1.223-1.860) were independent risk factors for bacterial pulmonary infection in PATIENTS with ACI (P＜0.05). The sensitivity and specificity of Prob were 85.00%, 67.50%, and the area under the curve was 0.812. Conclusion The majority of patients with ACI complicated with bacterial lung infection are gram-negative bacteria. Clinical attention should be paid to the sputum culture results of these patients, and appropriate antibiotic treatment should be applied according to the detection of pathogenic bacteria. The occurrence of bacterial lung infection is related to age, invasive operation, complete bed rest, NIHSS score at admission, large cerebral infarction and other factors. The prediction model based on the above factors has an important guiding role in predicting the risk of bacterial pulmonary infection in ACI patients