To　solve　the　problem　of　maneuvering　decision　in　the　autonomous　air　combat　of　unmanned　combat　aerial　vehicle,　the　existing　research　achievements　are　analyzed　and　a　maneuvering　decision　model　that　combines　optimization　idea　with　machine　learning　is　proposed.　The　multi-objective　optimization　method　is　used　as　the　core　of　decision　model,　which　solves　the　problem　of　setting　weight　for　multiple　optimization　targets　and　improves　the　extensibility　of　decision　model.　On　the　basis　of　multi-objective　optimization,　an　evaluation　network　is　trained　by　reinforcement　learning　and　used　for　auxiliary　decision-making　to　enhance　the　antagonism　of　decision　model.　In　order　to　test　the　performance　of　decision　model,　with　the　background　of　short-range　air　combat,　three　simulation　experiments　are　designed　to　test　the　feasibility　of　multi-objective　optimization　method,　the　effectiveness　of　auxiliary　decision　network　and　the　overall　performance　of　decision　model.　The　simulation　results　show　that　the　maneuvering　decision　model　can　be　used　in　real-time　confrontation　with　the　maneuvering　enemy　aircraft.　©　2018,　Editorial　Board　of　JBUAA.　All　right　reserved.