[Objective] This paper constructs an information demand mining framework of Chinese online health community users adapted to the big data environment, and analyzes the user information needs by taking the data of tumor-forum as an example. [Methods] The Latent Semantic Indexing (LSI) model and MapReduce distributed text clustering technology were used in this framework to mine the user information needs. We use all the Q&A data (24,305 in total) from tumor-forum of Chinese online health community (qiuyi.cn) as the experimental data source. [Results] The proposed framework mines the five information needs and their proportions of the tumor users: treatment (43.3%), pathology and etiology (34.5%), examination (12.1%), postoperative (7.0%), prevention (3.1%), and top 20 keywords of these needs. The analysis shows the growth of each needs, and the significant difference between domestic users and foreign users. Gender differences are also significant, the male need treatment information most, while female need pathological and etiological information most. Age difference is large too, and the information needs of young people are the largest (83.79%), etc. [Limitations] There may be better threshold selection, and the medical thesaurus is not prefect. The analysis of information needs is not multidimensional. [Conclusions] The proposed framework is feasible. The paper found the trend of the demand distribution changes with year and the distribution of users information needs vary with age or gender.