In　wastewater　treatment　processes,　building　performance　evaluation　models　to　predict　key　indicators　under　uncommon　operating　conditions　is　difficult　due　to　the　lack　of　labeled　data.　Domain　adaptation　can　be　used　to　solve　this　problem　through　leveraging　the　knowledge　of　common　conditions　to　construct　prediction　models　for　uncommon　conditions.　Considering　the　costs　of　labeling　data,　it　is　reasonable　to　assume　that　only　data　from　the　most　common　condition　are　labeled.　Therefore,　all　domain　adaptation　tasks　share　a　source　domain.　Under　this　assumption,　most　domain　adaptation　methods　require　mapping　the　same　source　data　multiple　times　and　training　multiple　task-specific　predictors　in　different　tasks,　resulting　in　additional　computational　costs.　To　give　a　solution,　a　stepwise　domain　alignment　strategy　is　proposed,　which　consists　of　two　steps.　First,　the　latent　features　of　source　domain　are　extracted,　and　the　features　are　fixed　after　this　step.　Second,　target　domains　from　different　tasks　are　mapped　to　the　fixed　feature　space　to　achieve　domain　alignment.　Based　on　the　strategy,　a　two-stage　multi-target　adversarial　adaptation　network　(TS-MAAN)　for　predicting　effluent　quality　index　is　proposed,　which　consists　of　an　autoencoder　and　a　generative　adversarial　network.　Additionally,　parameter　initialization　and　multi-kernel　maximum　mean　discrepancy　optimization　are　further　proposed　to　improve　the　stability　and　prediction　accuracy　of　the　TS-MAAN,　respectively.　Experiments　conducted　on　datasets　generated　by　the　Benchmark　Simulation　Model　No.1　demonstrate　that　TS-MAAN　exhibits　excellent　prediction　accuracy　and　stability,　while　enabling　efficient　multi-target　domain　adaptation.　Moreover,　these　experiments　verify　the　effectiveness　of　parameter　initialization　and　MK-MMD　optimization.　IEEE