A clustering method based on the dual linguistic information fusion is proposed to solve the conflict of clustering results caused by different sorts of information. Specifically, the experts’ similarities are calculated according to preference information, which can be employed to obtain the prior group classification preference. With the similarity vector of decision support information, the consistency and inconsistency indexes are introduced to present the extent of uniformity and difference of group clustering results from dual-dimension calculation. With the objective of minimizing the inconsistency measures of group clustering results, a programming model is constructed to calculate the attribute weights. Furthermore, the netting clustering method is utilized to determine the clustering results. Finally, a case study is conducted to illurstrate the rationality of the proposed method.