Objective We adopt heart rate variability (HRV) analysis method based on complex network theory to explore the variability of heartbeat kinetic caused by hypertension,which provides new thoughts to evaluate the cardiovascular system function for hypertensive patients.Methods This paper analyzes the HRV data from 17 cases of primary hypertension patients (from the 414th Hospital of Chinese People’s Liberation Army) and 17 cases of healthy older people.Firstly,by adopting phase space reconstruction,internal combined arrangement and surrogate data method,the very short-term heart rate variability sequence (50 points) of hypertension patients group and healthy control group are converted from time domain to network domain.Then we analyze the topological characteristic of network,and calculate the four fundamental network characteristic parameters: efficiency,clustering coefficient,average degrees and distribution entropy value.Results Three network character parameters of hypertension group,namely efficiency,average degree and distribution entropy values,significantly decrease in comparison with the healthy control group (P<0.05),especially for the distribution entropy parameter.The decrease of clustering coefficient in hypertension group is close to significant in comparison with the healthy control group (P=0.055).Conclusions Two changes may occur for the heartbeat dynamic systems of hypertensive patients: one is that the economic operation mode,which allows system both in overall collaboration and local focus,is destroyed;second is that the nonlinear and coupling of system at different times is weakened.