Revealing　the　relations　among　robotic　comprehensive　performance,　configuration,　scales　and　working　tasks　is　the　basis　to　optimize　robotic　mechanism.　Due　to　the　correlation　and　diversity　of　the　single　performance　indexes,　statistical　principles　of　linear　dimension　reduction　and　nonlinear　dimension　reduction　are　introduced　into　comprehensive　performance　analysis　and　evaluation　for　typical　serial　robot.　The　robotic　mechanism＇s　configuration,　scales　and　task　with　the　best　comprehensive　performance　can　be　obtained　by　principal　component　analysis　(PCA)　and　kernel　principal　component　analysis　(KPCA)　respectively.　The　results　show　that　KPCA　can　reveal　the　nonlinear　relations　among　different　single　performance　indexes　more　effectively　and　provide　more　comprehensive　performance　information　than　PCA.　Thus,　task-oriented　method　of　serial　robot　for　mechanism　analysis　and　evaluation　is　proposed,　which　also　provides　scientific　research　basis　for　the　mechanism　synthesis　and　optimum　task　order.