The 5G URLLC(ultra-reliable and low-latency communication) is leveraged in industrial field such as industrial control and autonomous driving that are sensitive to latency. Combined with 5G LAN technology, it facilitates the aggregation of dispersed equipment within a learning factory into a localized network, restricting access to equipment resources to users within a designated area. This approach minimizes data transmission latency and improves the confidentiality of data exchange. This paper designs a teaching management system based on 5G and identification resolution technology. The system is set against the background of the second-level node of the industrial internet identification resolution and establishes an identification resolution specification and local application node for the 5G learning factory environment. It provides a unique mapping of the industrial internet for the internal equipment and personnel resources of the learning factory. During the teaching process of the learning factory, teachers and administrator manage the equipment resources through resource identification. Students, after connecting to the local area network and registering, obtain the order of experimental learning and various teaching resources of the equipment. Now the system has been applied in the experimental teaching of AMTC 5G learning factory. It automatically generates identification codes for students, divides them into groups, and uses Johnson algorithms to generate a learning sequence for each group based on the running time of each device. After the learning, the system will evaluate the student’s proficiency of each device and score the level of learning situation of all students.