The　disadvantages　of　the　traditional　one-dimensional　convolution　neural　network　(1D-CNN)　model　based　on　honeysuckle　near-infrared　spectral　data　(NIRS)　include　high　parameter　quantity,　low　efficiency,　and　inability　to　identify　unknown　categories　effectively.　In　this　paper,　we　propose　a　one-dimensional　very　deep　convolution　neural　network　(1D-VD-CNN)　and　design　an　auto-encoder　mechanism　for　detecting　honeysuckle　from　unexplored　habitats.　First,　the　1D-VD-CNN　model　uses　the　efficient　very　deep　(VD)　structure　to　replace　the　hidden　layer　structure　in　the　traditional　1D-CNN　model.　The　model　can　be　directly　applied　to　analyze　one-dimensional　near-infrared　spectral　data　(NIRS).　Second,　combining　the　reconstruction　error　of　the　auto-encoder,　a　honeysuckle　identification　method　considering　an　unknown　origin　is　designed,　which　can　solve　the　problem　of　high　confidence　in　convolution　neural　networks　by　using　an　auto-encoder　and　reconstruction　errors　of　the　samples　to　be　tested.　Whether　the　sample　is　an　unknown　variety　can　be　determined　by　comparing　the　corrected　confidence　level　with　the　preset　threshold　value.　The　results　show　that　the　accuracy　of　the　1D-VD-CNN　training　set　and　test　set　is　100%,　and　the　loss　value　converges　to　0.001.　Compared　with　the　traditional　1D-CNN　model,　the　parameters　and　FLOPs　are　reduced　by　nearly　71%　and　8%,　respectively.　At　the　same　time,　compared　with　the　NIRS　analysis　and　the　PLS-DA　method,　the　1D-VD-CNN　model　has　higher　efficiency　and　better　recognition　performance　for　honeysuckle　near-infrared　spectral　classification.　Meanwhile,　the　accuracy　rate　of　the　auto-encoder　for　the　category　detection　mechanism　of　honeysuckle　from　an　unknown　origin　is　98%.　The　model　can　quickly　and　efficiently　classify　honeysuckle　from　different　habitats　and　detect　honeysuckle　from　unexplored　habitats.　©　2023　Elsevier　B.V.