In the real world, multi-label learning has become a hotspot in machine learning research area. In the multi-label learning problem, each instance is usually described by multiple class labels, which could be correlated with each other. It is well known that exploiting label correlations is important for multi-label learning. In this paper, an improved association rule mining algorithm based is designed on the matrix divide-and-conquer strategy. In addition, a proof is given to show the proposed algorithm in finding correct frequent items, and an application of the algorithm to the multi-label learning framework is also provided. Moreover, a global association rule mining and a local association rule mining based multi-label classification methods are proposed. Experimental results on several datasets show that the proposed methods can obtain a better classification performance on 5 different evaluation criteria.