A　continuous　Deep　Belief　Network　(cDBN)　with　two　hidden　layers　is　proposed　in　this　paper,　focusing　on　the　problem　of　weak　feature　learning　ability　when　dealing　with　continuous　data.　In　cDBN,　the　input　data　is　trained　in　an　unsupervised　way　by　using　continuous　version　of　transfer　functions,　the　contrastive　divergence　is　designed　in　hidden　layer　training　process　to　raise　convergence　speed,　an　improved　dropout　strategy　is　then　implemented　in　unsupervised　training　to　realize　features　learning　by　de-cooperating　between　the　units,　and　then　the　network　is　fine-tuned　using　back　propagation　algorithm.　Finally,　the　experiments　on　CATS　benchmark　and　waste　water　parameters　forecasting　show　that　cDBN　has　the　advantage　of　higher　accuracy.　simpler　structure　and　faster　convergence　speed　than　other　methods.