Based on the data of elderly body morphology, biochemical indexes, mental indexes, diet and other data for, and based on big data analysis, this study constructs the structure of the intelligent human body system, which contains the information entry area and the data collection area and so on, in order to realize the precise intervention. Through the wearable sensor technology, physiological indicators such as heart rate, pulse, blood oxygen, blood pressure, blood glucose, etc. are monitored, and the Qt5.9.3 platform is adopted to realize the hierarchical, componentized, generalized, extensible and cross-platform development of the software platform. The mathematical model of association rules and parameter estimation equations are established, and the association rules are designed by combining the nodes of Apriori algorithm to realize the monitoring and evaluation of sports health status. The study selected a total of 1,000 older adults in two communities in a first-tier city as the object, and the reliability test showed that the structural validity coefficients were greater than 0.7 and 0.8, and the average reliability of the intra-survey group and functional scores were 0.956 and 0.979. After the test, the intervention group test lung capacity (1.73±0.15)L/min, and the respiratory system was improved. In addition, a sound elderly exercise health precision intervention system is proposed, which provides a theoretical and practical reference for the intelligent and precise health management of the elderly.
