The　multivariate　statistical　method　such　as　principal　component　analysis　based　on　linear　dimension　reduction　and　kernel　principal　component　analysis　based　on　nonlinear　dimension　reduction　as　the　modified　principal　component　analysis　method　are　commonly　used.　Because　of　the　diversity　and　correlation　of　robotic　global　performance　indexes,　the　two　multivariate　statistical　methods　principal　component　analysis　and　kernel　principal　component　analysis　methods　can　be　used,　respectively,　to　comprehensively　evaluate　the　global　performance　of　PUMA560　robot　with　different　dimensions.　When　using　the　kernel　principal　component　analysis　method,　the　kernel　function　and　parameters　directly　have　an　effect　on　the　result　of　comprehensive　performance　evaluation.　Because　kernel　principal　component　analysis　with　polynomial　kernel　function　is　time-consuming　and　inefficient,　a　new　kernel　function　based　on　similarity　degree　is　proposed　for　the　big　sample　data.　The　new　kernel　function　is　proved　according　to　Mercer＇s　theorem.　By　comparing　different　dimension　reduction　effects　of　principal　component　analysis　method,　the　kernel　principal　component　analysis　method　with　polynomial　kernel　function,　and　the　kernel　principal　component　analysis　method　with　the　new　kernel　function,　the　kernel　principal　component　analysis　method　with　the　new　kernel　function　could　deal　more　effectively　with　the　nonlinear　relationship　among　indexes,　and　its　calculation　result　is　more　reasonable　for　containing　more　comprehensive　information.　The　simulation　shows　that　the　kernel　principal　component　analysis　method　with　the　new　kernel　function　has　the　advantage　of　low　time　consuming,　good　real-time　performance,　and　good　ability　of　generalization.