Recursive word topology structure is commonly found in natural language sentences, and discovering this structure can help us to not only identify the units that a sentence contains but also how they interact to form a whole. In this paper, we explore a novel recursive neural network (RNN) based word topology model (WordTM) for hierarchical phrase-based (HPB) speech translation, which captures the topological structure of the words on the source side in a syntactically and semantically meaningful order. Experiments show that our WordTM significantly outperforms the state-of-the-art soft syntactic constraints.