Online health forums provide a convenient way for patients to obtain medical information and connect with physicians and peers outside of clinical settings. However, large quan- Tities of unstructured and diversified content generated on these forums make it difficult for users to digest and ex- Tract useful information. Understanding user intents would enable forums to more accurately and efficiently find rele- vant information by filtering out threads that do not match particular intents. In this paper, we derive a taxonomy of intents to capture user information needs in online health forums, and propose novel pattern based features for use with a multiclass support vector machine (SVM) classifier to classify original thread posts according to their underly- ing intents. Since no dataset existed for this task, we employ three annotators to manually label a dataset of 1,200 Health- Boards posts spanning four forum topics. Experimental re- sults show that SVM with pattern based features is highly capable of identifying user intents in forum posts, reach- ing a maximum precision of 75%. Furthermore, comparable classification performance can be achieved by training and testing on posts from different forum topics (e.g. training on allergy posts, testing on depression posts). Finally, we run a trained classiffier on a MedHelp dataset to analyze the distribution of intents of posts from different forum topics.