In　label　distribution　learning　(LDL),　an　instance　is　involved　with　many　labels　in　different　importance　degrees,　and　the　feature　space　of　instances　is　accompanied　with　thousands　of　redundant　and/or　irrelevant　features.　Therefore,　the　main　characteristic　of　feature　selection　in　LDL　is　to　evaluate　the　ability　of　each　feature.　Motivated　by　neighborhood　rough　set　(NRS),　which　can　be　used　to　measure　the　dependency　degree　of　feature　via　constructing　neighborhood　relations　on　feature　space　and　label　space,　respectively,　this　article　proposes　a　novel　label　distribution　feature　selection　method.　In　this　article,　the　neighborhood　class　of　instance　in　label　distribution　space　is　defined,　which　is　beneficial　to　recognize　the　logical　class　of　target　instance.　Then,　a　new　NRS　model　for　LDL　is　proposed.　Specially,　the　dependency　degree　of　feature　combining　label　weight　is　defined.　Finally,　a　label　distribution　feature　selection　based　on　NRS　is　presented.　Extensive　experiments　on　12　data　sets　show　the　effectiveness　of　the　proposed　algorithm.