In　order　to　satisfy　the　heightened　emissions　regulation　and　further　enhance　the　performance　of　the　engine,　efforts　on　amelioration　of　the　engine　management　system　are　of　great　significance　besides　using　intelligent　regression　algorithms.　Based　on　a　hydrogen-enriched　Wankel　rotary　engine,　multiple　engine　operations　with　variable　excess　air　ratios,　variable　ignition　timing,　and　variable　hydrogen　enrichment　have　been　carried　out　a　series　of　engine　calibration　tests　in　detail.　After　recording　the　required　experimental　data,　three　different　methods,　including　quadratic　polynomial,　artificial　neural　networks　(ANN),　and　support　vector　machine　(SVM)　are　applied　to　construct　a　multi-objective　regression　model　which　gives　a　unique　insight　into　the　mathematical　relationship　between　the　engine　performance　and　the　operation　and　control　parameters.　For　the　ANN,　the　effect　of　the　number　of　nodes　in　the　hidden　layer　on　the　regression　performance　was　discussed,　and　the　weight　values　of　the　ANN　was　optimized　using　a　genetic　algorithm.　For　the　SVM,　the　effects　of　the　kernel　function　and　three　optimization　methods　on　regression　performance　were　discussed.　The　results　indicated　that　the　SVM　exhibited　the　best　fitting　results　among　the　three　methods.　The　optimal　R-2　for　brake　thermal　efficiency,　fuel　energy　flow　rate,　nitrogen　oxide,　carbon　monoxide,　and　unburned　hydrocarbon　is　0.9877,　0.9840,　0.9949,　0.9937,　and　0.9992,　respectively.　It　is　highly　recommended　that　the　SVM　method　as　a　generic　methodology　may　be　a　new　direction　for　nonlinear　control　system　modeling　of　the　Wankel　engine.