The　feature　extraction　method　is　an　effective　tool　to　understand　the　behavior　of　plug-flow　wastewater　treatment　process　(PF-WWTP).　However,　it　is　a　challenge　to　extract　feature　components　due　to　PF-WWTP　subjected　to　the　time-varying　system　with　dataset　mismatch.　To　solve　this　problem,　in　this　paper,　an　adaptive　feature　extraction　method　(AFEM)　based　on　dynamic　kernel　principal　component　analysis　(KPCA)　is　proposed　to　improve　the　feature　extraction　accuracy.　First,　a　data　adjustment　method　is　proposed　to　adapt　datasets　of　process　variables　to　the　different　hydraulic　residence　time.　Then,　the　matching　datasets　can　be　used　to　observe　the　dynamics　of　metabolism　within　PF-WWTP.　Second,　a　dynamic　KPCA　algorithm　based　on　iterative　calculation　is　introduced　to　obtain　the　contribution　of　feature　components　for　process　variables.　This　algorithm　can　update　the　order　of　feature　components　online　following　with　the　time-varying　flow-rates　of　PF-WWTP.　Third,　an　error-oriented　self-adaptive　mechanism　is　designed　to　determine　the　dimension　of　feature　components　for　process　variables.　This　mechanism　not　only　performs　preferable　feature　extraction　without　giving　thresholds　but　also　ensures　its　realtime　accuracy.　Finally,　AFEM　is　compared　with　some　existing　feature　extraction　methods　through　experiments.　The　results　show　that　the　proposed　AFEM　can　accurately　extract　feature　components　for　PF-WWTP.　©　2021　IEEE.