In　the　wastewater　treatment　process,　unsupervised　domain　adaptation　(UDA)　enables　cross-condition　prediction　for　key　performance　indicators.　However,　the　lack　of　interpretability　in　predictions　can　compromise　the　reliability　of　the　model.　This　work　proposes　a　transferable　ensemble　additive　network　(TEAN)　that　is　both　capable　of　solving　domain　adaptation　tasks　and　providing　interpretable　predictions.　TEAN　consists　of　an　ensemble　additive　network　(EAN)　and　a　transferable　additive　network　(TAN),　which　are　essentially　a　transfer　feature　learning　model　and　a　domain　adaptation　model　based　on　multikernel　maximum　mean　discrepancy　(MK-MMD),　respectively.　To　improve　the　performance　of　the　model　in　terms　of　domain　adaptation,　the　EAN　is　pretrained　to　learn　transfer　features,　and　the　latent　feature　dimensions　in　the　TAN　are　augmented　to　better　learn　and　capture　interdomain　discrepancies.　To　enhance　interpretability,　EAN　conducts　feature　selection　based　on　importance　to　obtain　sparse　feature　representations.　To　avoid　inconsistent　selection　results　across　multiple　runs　compromising　the　interpretability　of　the　model,　TEAN　constructs　weighted　variance　to　measure　the　importance　of　features　and　applies　an　ensemble　strategy　in　building　EAN.　Experimental　results　conducted　on　data　generated　from　benchmark　simulation　model　No.　1　(BSM1)　demonstrate　that　TEAN　outperforms　the　other　comparison　methods.　TEAN　achieves　more　consistent　feature　selection　results　under　multiple　runs　and　exhibits　excellent　prediction　accuracy　for　key　performance　indicators,　while　its　predictions　are　interpretable.