We propose a microvideo multilabel learning model based on a multiview low-rank representation, which combines the low-rank representation and multilabel learning into a unified framework and uses the consistency in different features to learn an intrinsically robust low-rank representation. Meanwhile, to represent the potential label correlations, our proposed model constructs a label correlation learning term to adaptively capture the labels’ correlation matrix. Furthermore, the supervised information is exploited to further improve the representation ability of our model. Extensive experiments on a large-scale public dataset show the effectiveness of the proposed scheme.