Since the Multi-Label K Nearest Neighbor (ML-KNN) classification algorithm ignores the correlation between labels, a multi-label classification algorithm by exploiting label correlation named CML-KNN was proposed. Firstly, the conditional probability between each pair of labels was calculated. Secondly, the conditional probabilities of predicted labels and the conditional probability of the label to be predicted were ranked, then the maximum was got. Finally, a new classification model by combining Maximum A Posteriori (MAP) and the product of the maximum and its corresponding label value was proposed and the new label value was predicted. The experimental results show that the performance of CML-KNN on Emotions dataset outperforms the other four algorithms, namely ML-KNN, AdaboostMH, RAkEL, BPMLL, while only two evaluation metric values are lower than those of ML-KNN and RAkEL on Yeast and Enron datasets. The experimental analyses show that CML-KNN obtains better classification results.