Objective To construct an intelligent recognition, prevention, and control system for infectious diseases and multidrug-resistant organism infections, aiming at improving the efficacy of full-process management, and to evaluate its application effects. Methods Based on personalized logic parsing rules that accurately reflect the infection status and transmission risks in real-time, an intelligent recognition, prevention, and control system with functions of automatic recognition, dynamic labeling, real-time sharing, early warning, and visual guidance was established. Patients undergoing invasive diagnostic and therapeutic procedures in two departments of a tertiary first-class hospital from October 2023 to May 2024 were selected as the research subjects. The differences in recognition, prevention, and control efficacy before and after the application of the system were compared using a self-controlled method, with traditional manual management as the control group and intelligent system management as the experimental group. Results A total of 2 146 patients were included in the analysis. The recognition, prevention, and control rate and the accuracy rate of recognizing infected individuals using the intelligent system were enhanced significantly compared with those using manual mode (improved from 5.3% and 72.4% to 100%, respectively), with statistical significance (both P<0.001). The median early warning time for infection information reached 85.20 days, with 100% early warning achieved. The average time spent by medical staff on infection information recognition and management was reduced by 4.71 hours per day. Conclusion The intelligent system constructed in this study significantly improves the effectiveness of full-process management in recognition, prevention, and control of infectious diseases and multidrug-resistant organism infection, effectively reduces the risk of cross-infection, and enhances the efficiency of diagnostic and therapeutic services.