Multi-label　image　classification　(MLIC)　is　a　quintessential　but　challenging　issue　in　the　field　of　Computer　Vision.　Since　the　label　co-occurrence　is　a　crucial　component　of　MLIC,　previous　existing　approaches　resort　to　the　label　co-occurrence　for　either　modeling　label　correlations　or　modeling　visual　feature　relationships.　However,　these　methods　ignore　either　the　feature　interaction　or　the　label　characteristics　in　MLIC.　In　this　paper,　we　propose　a　label-aware　graph　representation　learning　(LGR)　for　MLIC　that　can　explore　the　label　interaction　via　a　graph　neural　network　built　on　the　label　co-occurrence　and　mine　the　feature　correlations　via　another　graph　neural　network　also　based　on　the　label　co-occurrence.　Moreover,　to　decouple　semantic　visual　features,　current　approaches　resort　to　the　word　embedding　guided　semantic　decoupling　methods.　However,　the　word　embedding　cannot　clearly　represent　the　label　semantic　information　of　MLIC.　Hence,　we　reconstruct　the　semantic　decoupling　method　by　using　the　graph　label　representation.　Extensive　experiments　on　three　benchmark　datasets　well　demonstrate　that　our　proposed　framework　can　significantly　achieve　the　state-of-the-art　performance.　In　addition,　a　series　of　ablative　studies　further　demonstrate　the　positive　impacts　of　our　proposed　model.　©　2022　Elsevier　B.V.