Unmanned　aerial　vehicle　(UAV)　hyperspectral　remote　sensing　technology　has　developed　rapidly　in　recent　years,　providing　a　new　scheme　for　intelligent　monitoring　of　forest　resources.　This　study　aims　to　deeply　mining　the　UAV　hyperspectral　information,　and　construct　the　best　model　for　extracting　Moso　bamboo　(Phyllostachys　pubescens)　forest　information.　Firstly,　the　spectral　information　features　of　Moso　bamboo,　broadleaf,　bare　area　and　shadow　were　obtained　and　their　spectral　anisotropies　were　analyzed.　Secondly,　multiple　machine　learning　algorithms　were　combined　with　the　recursive　feature　elimination　(RFE)　to　obtain　three　sets　of　optimized　feature　subsets.　Eventually,　three　sets　of　optimization　features　were　substituted　into　multiple　machine　learning　algorithms　to　build　nine　models　of　Moso　bamboo　information　extraction,　comparing　their　classification　effects　and　generalization　capabilities.　The　results　showed　that:　(1)　The　subset　of　features　optimized　by　different　base　classifiers　in　combination　with　RFE　and　the　ranking　results　of　optimized　features’　importance　were　different,　but　all　the　three　sets　of　optimized　feature　subsets　showed　relatively　obvious　feature　difference　patterns;　(2)　The　nine　models　based　on　SVM-RFE,　RF-RFE　and　XGBoost-RFE　were　all　capable　of　extracting　Moso　bamboo　information,　among　which,　the　classification　effect　of　the　XGBoost-RFE-XGBoost　model　was　the　best　and　had　significant　advantages,　with　the　overall　classification　accuracy　OA,　Kappa　coefficient　and　R　2　of　98.75%,　0.983　and　0.980,　respectively,　and　the　classification　accuracy　for　Moso　bamboo　was　98.34%.　Thus,　XGBoost　was　the　most　applicable　algorithm　for　feature　optimization　in　combination　with　RFE,　and　the　most　effective　algorithm　with　a　better　generalization　ability　for　extracting　Moso　bamboo　information　from　UAV　hyperspectral　images.　This　study　is　crucial　for　Moso　bamboo’s　large　area　monitoring　and　fine　identification,　providing　a　technical　reference　for　obtaining　information　about　the　spatial　and　temporal　distribution　of　Moso　bamboo　resources　timely　and　accurately,　facilitating　the　monitoring　and　management　of　them,　and　giving　full　play　to　their　ecological　and　economic　benefits.　©　2023　The　Author(s).　Published　by　Informa　UK　Limited,　trading　as　Taylor　&　Francis　Group.