Data-driven　methods　for　predicting　quality　variables　in　wastewater　treatment　processes　(WWTPs)　have　mostly　ignored　the　slow　time-varying　nature　of　WWTP,　and　they　are　data-consuming　that　need　a　large　amount　of　independent　and　homogeneously　distributed　data,　which　makes　it　difficult　to　collect.　To　address　this　issue　with　few-shot　and　inconsistent　distribution,　a　transfer　learning　method　called　transferable　deep　slow　feature　network　(TDSFN)　for　time-series　prediction　is　proposed　by　leveraging　the　knowledge　of　relevant　datasets.　TDSFN　extracts　nonlinear　slow　features　of　WWTP　with　inertia　from　the　time　series　through　a　deep　slow　feature　network　and　constructs　the　domain　invariant　features　based　on　them.　Target　feature　attention　is　designed　in　TDSFN　to　enhance　the　predictor　adaptability　to　the　target　domain　by　assigning　weights　to　the　source　features　based　on　their　similarity　to　target　features.　Furthermore,　a　variational　Bayesian　inference　framework　is　introduced　to　learn　the　parameters　of　TDSFN.　The　effectiveness　of　TDSFN　is　verified　through　prediction　experiments　based　on　WWTP.