Communities are fundamental units of every social network; their structure and evolution are essential to understanding the structure and functionality of large networks. Also, community evolution prediction is an important task with various real-life applications in social network analysis. In this paper, we present a framework for modeling community evolution prediction in social networks. Each community is characterized by a wide range of structural features to describe community characteristics and a series of evolutionary events. A community matching algorithm is also proposed to efficiently identify and track similar communities over time. Experiments on different data sets prove that a high rate of community evolution prediction has been achieved.