To reduce the overload of human management, recently runtime self-adaptation is emerging as an important characteristic required by most intelligent software-intensive systems. Most methods are built upon the analysis of concepts of architecture and exploit some ?craft? from the perspective of qualitative analysis. However, these methods are often incapable of reasoning about the history of requested services, hence it is difficult to improve more efficiently software efficiency and predictability. Quantitative analysis based on the theory of stochastic processes would be a better option to depict the runtime environment more accurately. This paper presents a demonstration of the idea. In this paper, we employ the mathematic characteristic of Hidden Markov Model to achieve self-adaptation at runtime by means of modeling the behavior of users? requests and the runtime context. After analyzing the history of requested services and reconstructing the request sequence, the model responds to requests in a more efficient and rapid fashion.