Deep　belief　network　(DBN)　is　an　effective　deep　learning　model,　which　has　been　widely　used　to　analyze　big-data　characteristics,　extract　features　and　approximate　nonlinear　systems.　However,　due　to　the　deep　structure　and　numerous　parameters,　DBN　suffers　from　the　disadvantage　of　time-consuming　training　process　generally.　To　address　this　problem　and　improve　the　training　efficiency　without　reducing　accuracy,　this　paper　proposes　a　self-optimizing　deep　belief　network　with　adaptive-active　learning　(SODBN-AAL).　In　the　proposed　SODBN-AAL,　an　adaptive　learning　algorithm　of　hyper-parameters　is　designed　to　ensure　a　good　accuracy.　On　this　basis,　an　active　learning　algorithm　is　developed　based　on　event-triggered　strategy　to　improve　the　training　efficiency　by　extracting　effective　data　and　skipping　invalid　data.　As　a　self-optimizing　model,　SODBN-AAL　combines　the　advantages　of　both　adaptive　hyper-parameters　and　the　event-triggered　active　learning.　Two　simulation　experiments　on　the　benchmark　problem　and　water　quality　prediction　are　conducted　to　show　the　advantages　of　SODBN-AAL.　The　results　show　that,　compared　with　the　basic　DBN　model,　SODBN-AAL　averagely　improves　the　learning　accuracy　by　77.13%　and　learning　efficiency　by　84.83%.　©　2024　IEEE.