With the emerging of social network websites, more and more social network online games are booming. Players have more alternatives of VWs games, while platform providers suffer from the problems of high customer turnover rate and low-customer-loyalty. Therefore, building a churn prediction model to facilitate subsequent churn management and customer retention is the best core marketing strategy. In this paper, we put emphasis on modeling a hybrid classification, which takes monetary cost, user behavior and social neighbor features into consideration. The experimental results show that the proposed hybrid model is well-suited for this problem in virtual worlds comparing to the existing churn prediction methods applied in the traditional retail, and financial industry.