In　recent　years,　hyperspectral　image　super-resolution　has　attracted　the　attention　of　many　researchers　and　has　become　a　hot　topic　in　the　field　of　computer　vision.　However,　it　is　difficult　to　obtain　high-resolution　images　due　to　imaging　hardware　devices.　At　present,　many　existing　hyperspectral　image　super-resolution　methods　have　not　achieved　good　results.　In　this　paper,　we　propose　a　hyperspectral　image　super-resolution　method　combining　with　deep　residual　convolutional　neural　network　(DRCNN)　and　spectral　unmixing.　Firstly,　the　spatial　resolution　of　the　image　is　enhanced　by　learning　a　priori　knowledge　of　natural　images.　The　DRCNN　reconstructs　high　spatial　resolution　hyperspectral　images　by　concatenating　multiple　residual　blocks,　each　containing　two　convolutional　layers.　Secondly,　the　spectral　features　of　low-resolution　and　high-resolution　hyperspectral　images　are　linked　by　spectral　unmixing.　This　approach　aims　to　obtain　the　endmember　matrix　and　the　abundance　matrix.　The　final　reconstruction　result　is　obtained　by　multiplying　the　endmember　matrix　and　the　abundance　matrix.　In　addition,　in　order　to　improve　the　visual　effect　of　the　reconstructed　image,　the　total　variation　regularity　is　used　to　impose　constraints　on　the　abundance　matrix　to　enhance　the　relationship　between　the　pixels.　The　experimental　results　of　remote　sensing　data　based　on　ground　facts　show　that　the　proposed　method　has　good　performance　and　preserves　spatial　information　and　spectral　information　without　the　need　for　auxiliary　images.