Concept　similarity　measure　based　on　feature　vector　has　wide　application　in　various　fields,　but　the　problems　of　polysemy　and　synonym　existing　in　feature　vector　affect　the　similarity　measure.　We　present　a　feature　optimization　algorithm　based　on　Chinese　Wikipedia　which　can　reduces　this　effect.　First　we　build　a　POS　feature　dictionary　(POS-Dic)　and　a　POS　Tongyici　Cilin(POS-Cilin),　and　then　a　new　feature　vector　is　used　for　concept　similarity　measure.　Experiments　show　that　the　algorithm　effectively　reduces　the　influence　of　polysemy　and　synonym　on　the　concept　similarity　measure.　©　2017　IEEE.