With　the　rapid　development　of　WEB　applications,　the　demand　for　dynamically　adjusting　computing　resources　based　on　the　load　variation　is　increasing.　However,　most　of　the　traditional　WEB　systems　have　limited　ability　to　respond　to　load　changes.　In　order　to　solve　the　problem,　software　self-adaptation　technology　has　been　applied　to　the　resource　management　of　WEB　systems.　Many　researchers　have　tried　to　propose　various　software　self-adaptation　models,　all　of　which　contain　the　control　loop　＂Monitor-Analyze-Plan-Execute＂.　However,　the　＂Knowledge-base＂　of　these　models　is　based　on　predefined　strategy　or　configuration,　which　increases　the　complexity　of　system　development　and　maintenance.　In　this　paper,　machine　learning　is　applied　to　software　self-adaptation　through　a　case　study,　and　the　＂Knowledge-base＂　is　given　by　machine　learning,　which　greatly　reduces　the　workload　of　system　maintenance　and　rule　configuration.　The　results　show　the　case　based　on　machine　learning　can　construct　the　corresponding　management　strategy　according　to　WEB　system　runtime　status,　and　conduct　software　self-adaptive　management.