In multi-label learning, each sample is represented by a single instance and associates with multiple class labels. Most of existing multi-label learning algorithms explore label correlations globally, by assuming that the positive label correlations are shared by all examples. However, in practical applications, different samples share different label correlations, and there is not only positive correlation among labels, but also mutually exclusive one (i.e., negative correlation). To solve this problem, we propose a KNN multi-label classification algorithm based on local positive and negative label correlation, named PNLC. Firstly, we preprocess the feature vector of multi-label data and construct the most discriminative features for each class. Then, in the training stage, the PNLC algorithm constructs the positive and negative label correlation matrixes by using the truth label of each k-nearest neighbor for all the training samples. Finally, in the test phase, the k-nearest neighbors and corresponding positive and negative pairwise label correlations for each test example are identified to calculate the maximum posterior probability so as to make prediction. Experimental results show that the PNLC algorithm is obviously superior to other well-established multi-label classification algorithms on the yeast and image datasets.