For　hyperspectral　image　(HSI)　classification,　it　is　very　important　to　learn　effective　features　for　the　discrimination　purpose.　Meanwhile,　the　ability　to　combine　spectral　and　spatial　information　together　in　a　deep　level　is　also　important　for　feature　learning.　In　this　letter,　we　propose　an　unsupervised　feature　learning　method　for　HSI　classification,　which　is　based　on　recursive　autoencoders　(RAE)　network.　RAE　utilizes　the　spatial　and　spectral　information　and　produces　high-level　features　from　the　original　data.　It　learns　features　from　the　neighborhood　of　the　investigated　pixel　to　represent　the　whole　local　homogeneous　area　of　the　image.　In　addition,　to　obtain　more　accurate　representation　of　the　investigated　pixel,　a　weighting　scheme　is　adopted　based　on　the　neighboring　pixels,　where　the　weights　are　determined　by　the　spectral　similarity　between　the　neighboring　pixels　and　the　investigated　pixel.　The　effectiveness　of　our　method　is　evaluated　by　the　experiments　on　two　hyperspectral　data　sets,　and　the　results　show　that　our　proposed　method　has　a　better　performance.