With the continuous enhancement of people’s health awareness, the awareness of disease prevention began to improve, and the demand for intelligent health status monitoring is also increasing. At present, the health status monitoring mostly depends on hospital equipment, which has high cost and cannot realize remote analysis. Based on this, this paper studies and analyzes the application of wireless sensor network model based on big data ecosystem in intelligent health monitoring. ZigBee protocol is used to realize data transmission, wireless sensor is used to monitor human body temperature, heart rate, blood oxygen saturation, and other physiological indicators, human posture monitoring algorithm is used to monitor posture changes, which is combined with human physiological parameter information, and the long-term and short-term memory model is used to effectively use the time sequence information of data to realize the functions of physiological parameter measurement, wireless positioning, and information fusion. An experiment was designed to test the accuracy of the health monitoring system. The results show that the wireless sensor retention model can meet the design requirements in terms of the monitoring accuracy of body temperature, heart rate, and blood oxygen saturation index in intelligent health monitoring, with low cost and can be combined with human dynamic information.