Water　quality　prediction　is　an　indispensable　task　in　water　environment　and　source　management.　The　existing　predictive　models　are　mainly　designed　by　data-driven　artificial　neural　networks　(ANNs),　especially　deep　learning　models　for　large-scale　water　quality　prediction.　However,　the　state　of　water　environment　is　a　dynamic　process　where　the　stationarity　of　water　quality　data　suffers　from　time　variation　and　human　activities,　which　leads　to　a　poor　prediction　accuracy　because　ANNs　receive　whole　water　quality　data　passively,　including　abnormal　conditions.　We　consider　such　a　tough　problem　in　this　article　and　propose　an　event-triggered　deep　fuzzy　neural　network　(ET-DFNN)　to　pursue　the　better　performance　of　water　quality　prediction　in　the　complex　water　environment.　First,　a　deep　pretraining　model　is　constructed　to　extract　the　effective　features　from　raw　water　quality　data.　Second,　we　construct　a　DFNN　model　where　the　extracted　effective　features　are　considered　as　the　input　variables.　Third,　some　events　are　defined　to　characterize　the　abnormal　conditions　of　state　evolution　in　water　quality.　The　DFNN　is　trained　and　updated　using　different　learning　strategies　only　when　the　corresponding　events　are　triggered,　otherwise　it　ignores　the　current　data　sample　and　directly　goes　to　the　next　data　sample.　The　practical　data-based　experimental　results　show　that　the　ET-DFNN　achieves　better　prediction　performance　in　accuracy　and　efficiency　than　its　peers.　Especially,　the　training　efficiency　of　ET-DFNN　is　improved　by　57.94%　on　total　phosphorus　prediction　and　48.31%　on　biochemical　oxygen　demand　prediction,　respectively.　IEEE