To　investigate　the　influence　of　operation　parameters　on　the　cyclic　variation　of　the　Wankel　rotary　engine　with　hydrogen　enrichment,　an　intelligent　regression　model　based　on　the　support　vector　machine　(SVM)　was　implemented　to　predict　the　cyclic　variation.　For　modeling　the　prediction　model,　the　cyclic　variation　of　speed　(CoVn)　and　cyclic　variation　of　the　main　combustion　duration　(CoVCA10-90)　were　used　as　an　evaluator　for　idle　and　part　load　conditions,　respectively.　The　operation　conditions　including　main　fuel　type　(gasoline　and　n-butanol),　hydrogen　volume　percentage　(βH2),　excess　air　ratio　(λ),　ignition　timing　(IT)and　speed　were　used　as　independent　variables.　When　optimizing　the　prediction　model,　the　data　processing　method,　kernel　function,　loss　function　and　optimization　method　on　the　prediction　performance　were　discussed　in　detail.　The　results　indicated　that　an　optimized　model　can　be　obtained　by　using　genetic　algorithm　combined　with　[0,　1]　data　processing　method,　and　the　coefficient　of　determination,　mean　square　error　and　mean　absolute　percentage　error　of　CoVn　were　0.9904,　0.0783　and　0.3845%,　corresponding　to　CoVCA10-90　were　0.9972,　0.0197　and　1.1729%,　respectively.　For　the　CoVn,　gasoline　as　the　main　fuel　was　lower　than　the　n-butanol　at　the　same　operating　condition.　The　CoVn　at　high　speed　was　greater　than　that　at　low　speed.　When　operating　at　part　load　conditions,　the　CoVCA10-90　decreased　with　the　increasing　βH2,　and　first　decreased　and　then　increased　with　advancing　IT.　©　2021　Elsevier　Ltd