In view of the high computational complexity of opinion leader mining in social networks， an opinion leader recognition algorithm based on K-core decomposition， named CandidateRank （CR）， was proposed. Firstly， the opinion leader candidate set in a social network was obtained based on K-core decomposition method， so as to reduce the data size of opinion leader recognition. Then， a user similarity concept including location similarity and neighbor similarity was proposed， and the user similarity was calculated by K-core value， the number of entries， average K-core change rate and the number of user followers， and the global influence of the user in the candidate set was calculated according to the user similarity. Finally， opinion leaders were recognized by ranking users in the opinion leader candidate set by the global influence. In the experiment， two evaluation indexes of user influence predicted by Independent Cascade Model （ICM） and centrality were used to evaluate the opinion leader set selected by the proposed algorithm on three real datasets with different sizes. The results show that the proposed algorithm has the average user influence for the selected Top-15 users of 21.442， which is higher than those of the other three algorithms. In addition， compared to four K-core-related algorithms in correlation index， the results show that CandidateRank algorithm performs better in general. In summary， CandidateRank algorithm improves the accuracy while reducing the computational complexity.