Recently,　the　quality　of　fresh　water　resources　is　threatened　by　numerous　pollutants.　Prediction　of　water　quality　is　an　important　tool　for　controlling　and　reducing　water　pollution.　By　employing　superior　big　data　processing　ability　of　deep　learning　it　is　possible　to　improve　the　accuracy　of　prediction.　This　paper　proposes　a　method　for　predicting　water　quality　based　on　the　deep　belief　network　(DBN)　model.　First,　the　particle　swarm　optimization　(PSO)　algorithm　is　used　to　optimize　the　network　parameters　of　the　deep　belief　network,　which　is　to　extract　feature　vectors　of　water　quality　time　series　data　at　multiple　scales.　Then,　combined　with　the　least　squares　support　vector　regression　(LSSVR)　machine　which　is　taken　as　the　top　prediction　layer　of　the　model,　a　new　water　quality　prediction　model　referred　to　as　PSO-DBN-LSSVR　is　put　forward.　The　developed　model　is　valued　in　terms　of　the　mean　absolute　error　(MAE),　the　mean　absolute　percentage　error　(MAPE),　the　root　mean　square　error　(RMSE),　and　the　coefficient　of　determination　(R2).　Results　illustrate　that　the　model　proposed　in　this　paper　can　accurately　predict　water　quality　parameters　and　better　robustness　of　water　quality　parameters　compared　with　the　traditional　back　propagation　(BP)　neural　network,　LSSVR,　the　DBN　neural　network,　and　the　DBN-LSSVR　combined　model.