The　purpose　of　current　research　was　to　implement　an　intelligent　regression　model　and　multi-objective　optimization　of　performance,　combustion　and　emissions　characteristics　for　a　hydrogen-enriched　gasoline　rotary　engine.　The　brake　thermal　efficiency　(BTE),　fuel　energy　flow　rate　(E-f),　nitrogen　oxides　(NOX),　carbon　monoxide　(CO)　and　hydrocarbon　(HC)　were　predicted　by　intelligent　regression　model　with　hydrogen　volume　fraction　(alpha(H2)),　excess　air　ratio　(lambda)　and　ignition　timing　(IT)　as　independent　variables.　The　intelligent　regression　models　were　based　on　support　vector　machine　(SVM)　and　optimized　by　the　genetic　algorithm　(GA)　to　obtain　the　optimal　parameters　of　the　regression　model.　The　data　for　training　the　SVM　model　were　derived　from　the　experimental　results　of　a　hydrogen-enriched　rotary　engine,　in　which　the　speed　was　kept　constant　at　4500　r/min,　the　absolute　manifold　pressure　remained　at　35　KPa,　the　variation　of　alpha(H2),　lambda　and　IT　were　0-6%,　1-1.3　and　24-42　degrees　CA　before　top　dead　center　(bTDC),　respectively.　After　optimized　by　GA,　the　coefficient　of　determination　of　BTE,　E-f,　NOX,　CO　and　HC　between　the　SVM　model　and　the　corresponding　data　were　greater　than　0.98,　and　the　mean　absolute　percentage　error　were　＜1%.　The　performance,　combustion,　and　emissions　characteristics　including　BTE,　E-f,　NOX,　CO　and　HC　were　considered　for　multi-objective　optimization　to　obtain　higher　performance　and　lower　emissions,　and　were　solved　using　the　non-dominated　sorting　genetic　algorithm　II.　For　this　study,　when　the　Pareto-optimal　solutions　were　obtained,　the　optimal　operating　parameters　were　further　obtained　by　limiting　the　performance　and　emissions　parameters　with　the　alpha(H2)　of　5.06%,　lambda　of　1.09%,　and　IT　of　34.27　degrees　CA　bTDC.