Evolutionary　game　theory　is　widely　applied　in　network　attack　and　defense.　The　existing　network　attack　and　defense　analysis　methods　based　on　evolutionary　games　adopt　the　bounded　rationality　hypothesis.　However,　the　existing　research　ignores　that　both　sides　of　the　game　get　more　information　about　each　other　with　the　deepening　of　the　network　attack　and　defense　game,　which　may　cause　the　attacker　to　crack　a　certain　type　of　defense　strategy,　resulting　in　an　invalid　defense　strategy.　The　failure　of　the　defense　strategy　reduces　the　accuracy　and　guidance　value　of　existing　methods.　To　solve　the　above　problem,　we　propose　a　reward　value　learning　mechanism　(RLM).　By　analyzing　previous　game　information,　RLM　automatically　incentives　or　punishes　the　attack　and　defense　reward　values　for　the　next　stage,　which　reduces　the　probability　of　defense　strategy　failure.　RLM　is　introduced　into　the　dynamic　network　attack　and　defense　process　under　incomplete　information,　and　a　multistage　evolutionary　game　model　with　a　learning　mechanism　is　constructed.　Based　on　the　above　model,　we　design　the　optimal　defense　strategy　selection　algorithm.　Experimental　results　demonstrate　that　the　evolutionary　game　model　with　RLM　has　better　results　in　the　value　of　reward　and　defense　success　rate　than　the　evolutionary　game　model　without　RLM.　©　2021　Yanhua　Liu　et　al.