Since　the　sensitivity　of　neighborhood　method　for　irrelevant　features　is　high,　an　unsupervised　feature　selection　algorithm　based　on　neighborhood　preserving　learning(NPL)　is　proposed　by　utilizing　the　reconstruction　coefficient　of　neighborhood　to　maintain　the　original　data　structure.　Firstly,　according　to　the　similarity　of　each　data　and　its　neighborhood,　the　similarity　matrix　is　constructed　and　a　low　dimensional　space　is　built　by　introducing　a　mid-matrix.　Secondly,　an　effective　feature　subset　is　selected　by　the　Laplace　multiplier　method.　Finally,　the　proposed　algorithm　is　compared　with　six　state-of-the-art　feature　selection　methods　on　four　publicly　available　datasets.　Experimental　results　show　the　proposed　method　effectively　identifies　the　representative　features.　©　2018,　Science　Press.　All　right　reserved.