Recently,　convolutional　neural　networks　(CNNs)　have　demonstrated　impressive　capabilities　in　the　representation　and　classification　of　hyperspectral　remote　sensing　images.　Traditional　CNNs　require　massive　data　to　sufficiently　train　the　network.　To　tackle　this　problem,　graph　convolutional　network　(GCN)　has　been　introduced　for　hyperspectral　image　classification.　GCN　methods　usually　construct　the　graph　from　either　spectral　or　spatial　domain,　which　has　not　adequately　explored　the　information　in　the　joint　spectral-spatial　domain.　In　this　article,　we　propose　a　superpixel　spectral-spatial　feature　fusion　graph　convolution　network　for　hyperspectral　image　classification　(S3FGCN).　S3FGCN　can　comprehensively　use　information　in　spectral,　spatial,　and　spectral-spatial　domains　with　limited　data.　Moreover,　to　enhance　the　performance,　we　explore　a　shared　weights＇　GCN　in　the　spectral-spatial　domain.　To　further　improve　the　efficiency,　superpixels　are　used　to　construct　the　adjacency　matrix.　Finally,　dynamic　sampling　is　adopted　to　make　the　model　focus　more　on　difficult　samples.　In　the　experiments　on　four　datasets,　S3FGCN　demonstrates　better　accuracy　compared　with　the　state-of-the-art　hyperspectral　image　classification　methods.