Study on the relationship between students’ behavioral health and biomechanical indexes based on data mining

There is an interaction between behavior and health, and behavior monitoring information plays a role in reminding people of the need to maintain healthy behaviors or change unhealthy behaviors. Behavior monitoring information can explain the universality of behavior, analyze relevant influencing factors, determine specific actions to be followed, and carry out health education and publicity. It can also be used to trigger behavioral change trends, and evaluate and review health education and promotion programs. Behavioral monitoring information helps to explain changes in health status related to behavior. Because students are in a special period of physical and psychological development, their behavior is different from other periods. With the development of social culture, science and technology, the concept of modern health has changed from simple physical health to psychological and behavioral health. Based on this, this paper first investigated the definition and influencing factors of student behavior, focused on the definition of student behavior, expounded the classification and manifestation of student behavior, and analyzed the influencing factors and indicator system of student behavior. Then, from the construction of the student behavior monitoring system, it discussed the analysis and collection technology of student behavior data and the analysis and prediction model of student behavior, put forward the visualization model of student behavior big data, and discussed the design of the student behavior monitoring system based on data mining from two aspects, namely, the system functional requirements analysis and the overall system architecture design. Then the decision support algorithm was used to strengthen the detection of students’ health behavior. According to experiments and surveys, big data mining and decision support algorithms strengthen the construction of student behavior health monitoring system, and build a new student behavior health monitoring system, which is 33% more satisfied than the traditional student behavior health monitoring system.