Due　to　the　complex　dynamic　behavior　of　a　Wastewater　Treatment　Process　(WWTP),　the　existing　soft　sensing　models　usually　fail　to　efficiently　and　accurately　predict　its　effluent　water　quality.　Especially　when　a　lot　of　practical　data　is　provided　and　we　do　not　know　which　data-pair　is　more　valuable,　WWTP　modeling　becomes　a　time-consuming　process.　The　main　reason　is　that　the　existing　soft-sensing　models　update　their　parameters　at　each　data-pair　in　one　iteration,　while　some　update　operations　are　meaningless.　To　address　this　thorny　problem,　this　paper　proposes　a　Deep　Belief　Network　with　Event-triggered　Learning　(DBN-EL)　to　improve　the　efficiency　and　accuracy　of　soft-sensing　model　in　WWTP.　First,　some　events　are　defined　according　to　different　running　condition　during　the　process　of　training　DBN-based　soft　sensing　model.　The　different　running　condition　is　dominated　by　the　fluctuation　of　error-reduction　rate.　Second,　an　event-triggered　learning　strategy　is　designed　to　construct　DBN-EL,　whose　parameters　are　updated　only　when　a　positive　event　is　triggered.　Thirdly,　we　present　the　convergence　analysis　of　DBNEL　based　on　the　optimization　in　a　Markov　process.　Finally,　the　effectiveness　of　DBN-EL　is　demonstrated　on　soft-sensing　of　total　phosphorus　concentration　in　a　practical　WWTP　system.　In　experiment,　DBN-EL　is　compared　with　nine　different　models　on　soft-sensing　of　WWTP.　The　experimental　results　show　that　the　efficiency　of　DBN-EL　is　27.6%-64.9%　higher　than　that　of　nine　competitive　models,　which　indicates　that　the　proposed　model　is　readily　available　for　industrial　deployment　(c)　2021　Elsevier　B.V.　All　rights　reserved.