The　majority　of　human　action　recognition　methods　use　multifeature　fusion　strategy　to　improve　the　classification　performance,　where　the　contribution　of　different　features　for　specific　action　has　not　been　paid　enough　attention.　We　present　an　extendible　and　universal　weighted　score-level　feature　fusion　method　using　the　Dempster-Shafer　(DS)　evidence　theory　based　on　the　pipeline　of　bag-of-visual-words.　First,　the　partially　distinctive　samples　in　the　training　set　are　selected　to　construct　the　validation　set.　Then,　local　spatiotemporal　features　and　pose　features　are　extracted　from　these　samples　to　obtain　evidence　information.　The　DS　evidence　theory　and　the　proposed　rule　of　survival　of　the　fittest　are　employed　to　achieve　evidence　combination　and　calculate　optimal　weight　vectors　of　every　feature　type　belonging　to　each　action　class.　Finally,　the　recognition　results　are　deduced　via　the　weighted　summation　strategy.　The　performance　of　the　established　recognition　framework　is　evaluated　on　Penn　Action　dataset　and　a　subset　of　the　joint-annotated　human　metabolome　database　(sub-JHMDB).　The　experiment　results　demonstrate　that　the　proposed　feature　fusion　method　can　adequately　exploit　the　complementarity　among　multiple　features　and　improve　upon　most　of　the　state-of-the-art　algorithms　on　Penn　Action　and　sub-JHMDB　datasets.　(c)　The　Authors.　Published　by　SPIE　under　a　Creative　Commons　Attribution　3.0　Unported　License.　Distribution　or　reproduction　of　this　work　in　whole　or　in　part　requires　full　attribution　of　the　original　publication,　including　its　DOI.