An online hierarchical non-negative matrix fraction method was proposed to address the problem of flat topic structure of text stream topic detecting methods. At every time slot, the topic nodes were oplited-splited according to the normalized discounted cumulative gain and a topic hierarchy was built by iteratively assigning documents to the most related topic nodes. The hierarchy construction process refers the previous topic hierarchy. The underlying assumption is that the topics are evolving among the similar topic nodes in different time slots. The method can detect and track topics in stream in an online way, which reveals many useful relationships between the topics. Experiments on Nist TDT2 dataset show that our method outperforms the contrasting methods under different metrics, e.g. NMI, Micro F1, MAP and NDCG, and uses less execution time.