Due　to　its　ability　to　capture　long-range　dependencies,　self-attention　mechanism　based　transformer　models　are　introduced　for　hyperspectral　image　classification.　However,　the　self-attention　mechanism　has　only　spatial　adaptability　but　ignores　channel　adaptability,　thus　cannot　well　extract　complex　spectral-spatial　information　in　hyperspectral　images.　To　tackle　this　problem,　in　this　paper,　we　propose　a　novel　spectral-spatial　large　kernel　attention　network　(SSLKA)　for　hyperspectral　image　classification.　SSLKA　consists　of　two　consecutive　cooperative　spectral-spatial　attention　blocks　with　large　convolution　kernels,　which　can　efficiently　extract　features　in　spectral　and　spatial　domains　simultaneously.　In　each　cooperative　spectral-spatial　attention　block,　we　employ　the　spectral　attention　branch　and　the　spatial　attention　branch　to　generate　the　attention　maps,　respectively,　and　then　fuse　the　extracted　spatial　features　with　the　spectral　features.　With　large　kernel　attention,　we　can　enhance　the　classification　performance　by　fully　exploiting　local　contextual　information,　capturing　long-range　dependencies,　as　well　as　be　adaptive　in　the　channel　dimension.　Experimental　results　on　widely　used　benchmark　datasets　show　that　our　method　achieves　higher　classification　accuracy　in　terms　of　overall　accuracy,　average　accuracy,　and　Kappa　than　several　state-of-the-art　methods.　IEEE