Millions of people now participate in on line games, placing tremendous and often unpredictable maintenance burdens on their operators. Thus, understanding the dynamic behaviors of a player is critical for the systems, network, and designers. To the best of our knowledge, little work builds character interaction model based on the data stream mining. This work improves our understanding the behaviors of avatar/player in a on line game by collecting the behavior data, extracting frequent behavior patterns, learning the hidden hints and making good prediction on responses to the unexpected impacts. Besides, we develop two efficient approaches for mining the behavior data to find the interesting behavior pattern for future prediction on responses of opponents. Our novel findings include the following: One, due to the constraints of limited resources of time, memory, and sample size, MSS-MB was proposed to meet these conditions; the other, due to the constraints of real-time and on-line, there may have some errors occurred in the processing period, MSS-BE was proposed to control the errors as needed. Finally, based on the experimental results, we can predict the responses of opponents efficiently in the on line game.