According　to　the　specific　characteristics　of　samples,　dynamic　classifier　ensemble　chooses　appropriate　classifier　for　decision-making,　which　improve　classification　accuracy　effectively,　but　increase　the　cost　of　running　time.　Therefore,　Dynamic　Combination　of　Multiple　Classifiers　Based　on　Central　Similarity　is　proposed　in　this　paper,　which　chooses　different　members　classifier　according　to　the　similarity　between　classification　samples　and　each　class　center　to　avoid　validation　process　of　neighborhood　samples,　and　at　the　same　time,　adjust　each　corresponding　weights　to　improve　accuracy　furthermore.　The　experiments　demonstrate　that　this　algorithm　reduces　the　running　time　as　well　as　improve　the　accuracy　of　integration　classification,　besides,　choice　of　classifiers　don＇t　depend　on　neighborhood　samples　any　more,　so　it　shows　a　higher　accuracy　of　classification　for　small　scale　sample　training　set.　©　2011　IEEE.