Under different language contexts, people choose different terms or phrases to express their feelings and opinions. When a user is writing a paper or chatting with a friend, he/she applies a specific language model corresponding to the underlying goal. This paper presents a log-based study of analyzing the language models with specific goals. We exhibit the statistical information of terms and software programs, propose some methods to estimate the divergence of language models with specific user goals and measure the discrimination of these models. Experimental results show that the language models with different user goals have large divergence and different discrimination. These study conclusions can be applied to understand user needs and improve Human-Computer Interaction (HCI).