Visual　words　image　representation　has　been　very　popular　in　tag　ranking　of　social　image.　However,　the　descriptive　power　of　detected　features　is　not　evaluated　in　traditional　visual　words　model,　resulting　in　many　non-descriptive　local　features　are　clustered　as　visual　words.　Moreover,　the　time　consumption　will　be　increased　during　quantification　phase　due　to　compare　with　all　visual　words.　To　overcome　these　problems,　the　descriptive　visual　word　tree　is　created　for　tag　ranking　of　social　image　in　this　paper.　Firstly,　the　social　image　saliency　region　is　detected　with　visual　attention　model.　Then,　the　descriptive　visual　word　tree　can　be　created　by　clustering　the　descriptive　Scale　Invariant　Feature　Transform　(SIFT)　features　which　consist　of　refined　SIFT　features　and　corner　SIFT　features　extracted　from　saliency　region.　Finally,　the　tags　of　weighted　neighbor　images,　which　are　found　by　descriptive　visual　word　tree,　are　applied　to　rank　the　tags.　Experimental　results　indicate　that　the　proposed　descriptive　visual　word　tree　can　effectively　improve　the　accuracy　of　tag　ranking　of　social　image　as　well　as　reduce　the　time　of　tag　ranking.