Missing　data　imputation　is　a　critical　data　processing　procedure　in　wastewater　treatment　process.　However,　the　existing　imputation　methods　cannot　stand　the　missing　data　with　high　proportions　that　frequently　happens　due　to　unmaintained　instruments　or　detection　failures.　Transfer　Learning　aims　to　learn　much　reliable　information　for　the　target　domain　with　previous　learned　knowledge　from　a　source　domain,　which　provides　a　framework　for　solving　such　problem.　This　paper　proposes　a　filter　transfer　learning　algorithm　(FTLA)　for　missing　data　imputation　with　high　proportions.　First,　a　knowledge　acquisition　strategy　is　developed　to　extract　the　source　knowledge,　including　independent　knowledge　from　historical　datasets　and　parallel　knowledge　in　terms　of　related　datasets.　The　missing　data　is　then　interpreted　through　source　knowledge　comprehensively.　Second,　a　filter　transfer　learning　algorithm　is　designed　to　achieve　target　knowledge　that　mimics　the　tendency　of　the　missing　data.　This　algorithm　can　avoid　serious　negative　transfer　by　using　the　extended　Kalman　filter　to　filtrate　source　knowledge.　Third,　a　knowledge　rolling　mechanism　is　established　to　perform　the　imputation　online　with　target　knowledge,　which　can　maintain　the　reliable　imputation　for　missing　data　with　high　proportions.　Finally,　several　comparative　experiments　of　wastewater　data　are　provided　to　demonstrate　the　merits　of　missing　data　imputation　with　FTLA.