Mining user intents in online interactive behavior from social media data can effectively identify users’ motives behind communication and provide valuable information to aid medical decision-making and improve services. However, it is a challenging task due to the ambiguous semantic, irregular expressions and obscure intention classification categories. In this paper, we first define user intent categories based on speech act theory. On the basis of this, we develop a novel method to further classify users’ utterances according to their pragmatic functions. First, we design topic independent features by regularizing the utterance and categorizing the textual features. Then, we build a hierarchical model based on Hidden Markov Model (HMM)  to mine user intents in context sequence at both sentence and microblog level. Finally, we construct a dataset of microblogs about hot topics related to the medical event by a semi-automatic method. Experimental study shows the effectiveness of our method.