In　recent　years,　with　the　rapid　development　of　the　Internet　in　China,　online　transactions　have　grown　greatly.　For　example,　OTAs　with　a　large　number　of　hotels　have　accumulated　a　large　amount　of　hotel　data　and　user　consumption　data.　And　the　online　sales　of　hotels　is　the　basis　and　core　of　revenue　management.　Time　series　prediction　has　always　been　one　of　the　main　application　fields　of　machine　learning　algorithm.　From　the　classical　traditional　time　series　prediction　methods　to　long-term　and　short-term　memory　networks　and　closed-loop　neural　networks,　the　prediction　ability　is　constantly　improving.　With　the　development　of　deep　neural　networks,　convolution　neural　networks　show　superior　performance　in　the　prediction　of　time　series.　This　paper　proposes　a　new　prediction　model　based　on　the　improved　WaveNet　using　not　only　the　parameters　of　historical　sales　and　hotel　property,　but　also　the　parameters　of　holiday　time　and　time　position　in　the　prediction　range,　which　are　processed　by　serialization.　Simulation　results　are　presented　in　details　in　this　paper,　where　these　results　indicate　the　effectiveness　of　the　proposed　forecasting　tool　as　an　accurate　technique.　©　2019　Published　under　licence　by　IOP　Publishing　Ltd.