Objective To evaluate the knowledge capability of the large language model (LLM) ChatGPT-4o in the field of multidrug-resistant organism infection prevention and control, and explore its potential applications in healthcare-associated infection (HAI)control. Methods A professional knowledge question-and-answer dataset covering 5 categories (basic concept, transmission risk identification, infection control practice, antimicrobial stewardship, surveillance evaluation) was constructed based on guidelines and expert consensus. The dataset was input into ChatGPT-4o to generate responses. Four experts in the HAI management field evaluated the model’s output results using a 5-point Likert scale. The accuracy, completeness, and usability of LLM in the field of multidrug-resistant organism infection prevention and control were compared and analyzed. Results Among the 50 questions, ChatGPT-4o achieved an overall accuracy rate of 58.0%, a partially accuracy rate of 40.0%, and an inaccuracy rate of 2.0%. ChatGPT-4o performed best on "basic concept" questions, with an accuracy rate of 86.0%, and worst on "antimicrobial stewardship" questions, with an accuracy rate of 40.0%. The average scores for accuracy, completeness, and usability were 4.63, 4.70, and 4.57 points, respectively. Conclusion ChatGPT-4o demonstrates an excellent response capability for basic knowledge in the field of multidrug-resistant organism infection prevention and control. Although it has limitations such as content generalization and insufficient decision support ability in complex infection control situations, LLM in the field of HAI control has broad application prospects in the future.