In　the　design　of　complex　engineering　systems,　the　high　computational　cost　of　high-precision　simulation　technology　is　mostly　reduced　by　constructing　surrogate　models.　Previous　studies　have　proposed　hybrid　surrogate　models　to　improve　the　accuracy　and　expand　the　scope　of　application　of　a　specific　single　surrogate　model.　However,　it　has　been　observed　that　the　global　average　hybrid　surrogate　model　is　difficult　to　adapt　to　the　local　characteristics　of　the　prediction　points,　and　the　point-by-point　weighted　hybrid　surrogate　model　may　have　poor　global　fitting　accuracy.　In　this　paper,　a　novel　hybrid　surrogate　modeling　method　is　proposed　by　combining　global　and　local　error　measures,　which　is　called　pointwise　weighted　hybrid　surrogate　model　based　on　hybrid　measures　(PWHSMHM).　In　the　PWHSMHM,　the　benchmark　model　is　selected　by　leave-one-out　(LOO)　cross-validation,　and　the　local　error　measure　algorithm　of　surrogate　model　(LEMASM)　is　proposed　to　estimate　the　local　uncertainty　of　the　sub-proxy　model　based　on　the　sample　density　near　the　prediction　point.　The　relationship　between　the　global　error　measure　and　the　local　error　measure　is　adaptively　weighted　by　the　global　weight　coefficient.　The　experimental　results　on　40　benchmark　functions　confirm　that　PWHSMHM　not　only　has　higher　fitting　accuracy　than　single　surrogate　models　but　also　outperforms　existing　hybrid　surrogate　models　in　this　regard.　Finally,　the　proposed　method　is　applied　to　optimize　the　automobile　front　cover　to　prove　that　it　can　be　a　new　choice　for　the　design　optimization　of　complex　engineering　systems.