Manifold ranking has been successfully applied in query-oriented multi-document summariza-tion. It not only makes use of the relationships among the sentences but also the relationships between the given query and the sentences. However the information of original query is often insufficient. So we present a query expansion method which is combined in the manifold rank-ing to resolve this problem. Our method not only utilizes the information of the query term itselfand the knowledge base WordNet to expand it by synonyms but also uses the information of the document set itself to expand the query in various ways (mean expansion variance expansionand TextRank expansion). Compared with the previous query expansion methods our methodcombines multiple query expansion methods to better represent query information and at the same time it makes a useful attempt on manifold ranking. In addition we use the degree of wordoverlap and the proximity between words to calculate the similarity between sentences. We per-formed experiments on the datasets of DUC 2006 and DUC2007 and the evaluation results showthat the proposed query expansion method can significantly improve the system performance andmake our system comparable to the state-of-the-art systems.