In order to explore the topological changes of Normal Aging human Brain Functional Network (NABFN), a network evolution Model based on Naive Bayes (NBM) was proposed. Firstly, the probability of existing edges between nodes was defined based on link prediction algorithm of Naive Bayes (NB) and anatomical distance. Secondly, based on the brain functional networks of young people, a specific network evolution algorithm was used to obtain a simulation network of the corresponding middle-aged and old-aged gradually by constantly adding edges. Finally, a network Similarity Index (SI) was proposed to evaluate the similarity degree between the simulation network and the real network. In the comparison experiments with network evolution Model based on Common Neighbor (CNM), the SI values between the simulation networks constructed by NBM and the real networks (4.479 4, 3.402 1) are higher than those of CNM (4.100 4, 3.013 2). Moreover, the SI value of both simulation networks are significantly higher than those of simulation networks derived from random network evolution algorithm (1.892 0, 1.591 2). The experimental results confirm that NBM can predict the topological changing process of NABFN more accurately.