To deal with Two-Sided Matching (TSM) problem based on Hesitant Fuzzy Value (HFV) of unknown weights, a multi-attribute matching decision making approach was proposed. To begin with, the weight information was determined by maximizing the sum of deviations of the given values in terms of HFVs with multi-attribute evaluated by both two-sided Agents. Then, the matching degree could be aggregated via an operation of adjusted hesitant fuzzy weighted averaging with obtained weights and multi-attribute information. In addition, a multi-objective optimization model was established based on the matching degree of two sides. By solving this model into single objective optimization model in min-max method, the matching scheme was generated. Finally, a numerical illustration and comparison was taken, the solutions of objectives by the proposed method were respectively 1.689 and 1.575, and a unique matching scheme was obtained. The experimental results show that the proposed method can avoid multiple solutions caused by subjective weights of goal functions.