As scientific research is transitioning from traditional paradigms to an “AI for Science Paradigm”, university research-oriented laboratories are facing a series of challenges in resource allocation, instruments and equipment management, scheduling management, reservation management, and operational management. This paper proposes measures to optimize the open management of research-oriented laboratories to meet research needs based on the AI for Science Paradigm, including performance-oriented laboratory resource allocation management based on DEA, optimization of the configuration management based on AHP and interdisciplinary application management of instruments and equipment, the application of the Internet of Things (IoT) in the management of instruments and equipment, network security management based on machine learning, point-based management for usage rights, and mobile management for usage requirements.