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With the continuous development of the economy, science and technology have achieved unprecedented development along with the improvement of the economy. The first thing to bear is the continuous popularization and application of the Internet, which drives the development of Internet technology. Many online learning platforms currently used by universities only focus on the teacher system and teaching system. Compared with other subjects, there are fewer learning materials on the Internet for gymnastics teaching, and the lack of systematic teaching is the primary problem that most schools need to overcome. The current method of online learning has changed the relatively scarce phenomenon of resources in face-to-face teaching in the past, and has excellent improvements in the organization and improvement of resources. By constructing the framework of a professional teaching resource library, collecting and properly sorting out learning materials, and at the same time designing and producing network multimedia courseware to facilitate the learning of scholars. Network teaching technology is realized by the combination of Internet and multimedia equipment. This technology can not only give play to the advantages of computers in network data transmission, but also embody a new teaching model with teachers and students as the main body. Therefore, adjusting teaching methods, building a better learning space, and using CAI courseware learning resources connected by campus network is an important way for students to make compensatory learning, and it is also the main direction for scientific and technological progress in gymnastics teaching. The experimental results prove that the online multimedia learning platform can provide students with more gymnastics learning resources, increase students' interest and time in learning; it can effectively improve the teaching quality of teachers, and use the convenient interactivity of the network to realize a resource library The rapid update of the content enables the teaching content to keep up with the needs and pace of the development of the times.

The development and progress of multi-sensor data fusion theory and method also lay the foundation for the research of human posture tracking system based on inertial sensor. This paper mainly studies the simulation of gymnastic performance based on MEMS sensors. In the preprocessing of reducing noise interference, this paper mainly uses median filter to remove signal burr. In this paper, the use of virtual character model for gymnastics performance. The computer receives sensor data from the sink node of the motion capture device through a Bluetooth communication module. The unit calculates the quaternion output from the dynamic link library of sensor data processing, calculates the rotation and coordinate offset of the limb where each sensor node is located, and realizes the real-time rendering of the virtual human model by using the driver of the human model. At the same time, it controls the storage of sensor data, the driving of model and the display of graphical interface. When the gesture action is about to happen, a trigger signal is given to the system to mark the beginning of the action, so as to obtain the initial data of each axis signal of MEMS sensor. When the gesture action is completed, a signal to end the action is given to the system to mark the end of the action, so that the original signal data between the beginning and end of the gesture action can be captured. In order to ensure the normal communication between PS and PL, it is necessary to test the key interface. Because the data received by the SPI acquisition module is irregular, it is unable to verify whether the data is wrong. Therefore, the SPI acquisition module is replaced with an automatic incremental data module, and it is generated into an IP core to build a test platform for testing. The data show that the average measurement errors of x-axis displacement, Y-axis displacement, z-axis displacement and three-dimensional displacement are 8.17%, 7.51%, 9.72% and 8.7%, respectively. The results show that the MEMS sensor can accurately identify the action with high accuracy.