The　current　global　water　environment　has　been　seriously　damaged.　The　prediction　of　water　quality　parameters　can　provide　effective　reference　materials　for　future　water　conditions　and　water　quality　improvement.　In　order　to　further　improve　the　accuracy　of　water　quality　prediction　and　the　stability　and　generalization　ability　of　the　model,　we　propose　a　new　comprehensive　deep　learning　water　quality　prediction　algorithm.　Firstly,　the　water　quality　data　are　cleaned　and　pretreated　by　isolation　forest,　the　Lagrange　interpolation　method,　sliding　window　average,　and　principal　component　analysis　(PCA).　Then,　one-dimensional　residual　convolutional　neural　networks　(1-DRCNN)　and　bi-directional　gated　recurrent　units　(BiGRU)　are　used　to　extract　the　potential　local　features　among　water　quality　parameters　and　integrate　information　before　and　after　time　series.　Finally,　a　full　connection　layer　is　used　to　obtain　the　final　prediction　results　of　total　nitrogen　(TN),　total　phosphorus　(TP),　and　potassium　permanganate　index　(COD-Mn).　Our　prediction　experiment　was　carried　out　according　to　the　actual　water　quality　data　of　Daheiting　Reservoir,　Luanxian　Bridge,　and　Jianggezhuang　at　the　three　control　sections　of　the　Luan　River　in　Tangshan　City,　Hebei　Province,　from　5　July　2018　to　26　March　2019.　The　minimum　mean　absolute　percentage　error　(MAPE)　of　this　method　was　2.4866,　and　the　coefficient　of　determination　(R-2)　was　able　to　reach　0.9431.　The　experimental　results　showed　that　the　model　proposed　in　this　paper　has　higher　prediction　accuracy　and　generalization　than　the　existing　LSTM,　GRU,　and　BiGRU　models.