Hyperspectral　images　are　characterized　by　continuous　bands,　high　dimensionality,　large　data　volume　and　strong　correlation　between　adjacent　bands,　which　can　provide　richer　detailed　information　for　feature　classification.　However,　there　is　a　lot　of　redundant　information　and　noise　in　data,　and　the　direct　use　of　all　band　features　without　effective　analysis　and　selection　in　image　classification　will　lead　to　low　computational　efficiency　and　high　computational　complexity,　and　the　＂Hughes　phenomenon＂　that　the　classification　accuracy　may　increase　and　then　decrease　with　the　increase　of　band　dimension.　In　order　to　quickly　extract　a　subset　of　features　with　good　recognition　ability　from　hyperspectral　images　with　tens　or　even　hundreds　of　bands　to　avoid　the　＂dimensional　disaster＂.　This　paper　combines　the　filtered　ReliefF　algorithm　and　the　wrapped　recursive　feature　elimination　algorithm　(Recursive　feature　elimination,　RFE)　to　build　the　ReliefF-RFE　feature　selection　algorithm,　which　can　be　used　for　feature　selection　in　hyperspectral　image　classification.　The　algorithm　uses　the　ReliefF　algorithm　to　quickly　eliminate　many　irrelevant　features　based　on　weight　thresholds　to　narrow　and　optimize　the　range　of　feature　subsets.　The　RFE　algorithm　is　used　to　further　search　for　the　optimal　feature　subsets,　and　the　recursive　elimination　of　the　less　relevant　features　and　redundant　to　the　classifier　in　the　narrowed　feature　subsets　is　performed　to　obtain　the　feature　subsets　with　the　best　classification　performance.　In　this　paper,　three　standard　datasets,　including　the　Indian　pines　dataset,　Salinas-A　dataset　and　KSC　dataset,　are　used　as　experimental　data　to　compare　the　application　effect　of　the　ReliefF-RFE　algorithm　with　ReliefF　and　RFE　algorithms.　The　results　show　that　the　hyperspectral　image　classification　by　applying　the　ReliefF-RFE　algorithm　has　an　average　overall　accuracy　(OA)　of　92.　94%　F-measure　of　92.　81%　,　and　Kappa　coefficient　of　91.　94%;　in　the　three　datasets,　the　average　feature　dimension　of　ReliefF-RFE　algorithm　is　37%　of　that　of　ReliefF　algorithm,　while　the　average　operation　time　is　75%　of　that　of　the　RFE　algorithm.　It　shows　that　the　ReliefF-RFE　algorithm　can　ensure　the　classification　accuracy　while　overcoming　the　defects　of　the　filtered　ReliefF　algorithm,　which　cannot　effectively　reduce　the　redundancy　among　features　and　the　wrapped　RFE　algorithm,　which　has　high　time　complexity　and　has　a　more　balanced　comprehensive　performance,　which　is　suitable　for　feature　selection　in　hyperspectral　image　classification.