Clique percolation method has been used in many cases for discovering overlapping communities. However, its result is largely affected by parameters; as community size or cohesion changes the parameters needed shift accordingly. To overcome the dependency on parameters, in this paper we propose an approach for community evolution discovery based on community tree constructed by clique percolation: a community tree provides a hierarchical structure of communities discovered under a range of parameters in a given network; related community states can be found by searching a series of community trees thus the life span of a community can be discovered. We apply it to the word association network from DBLP data set and analyze how each community evolves. The outcome shows that this approach can effectively discover the community evolution process and identify the changes in size, membership and intensity. It’s also observed that communities of different size have different evolving characteristic features.