The　increase　of　network　size　and　sensory　data　leads　to　many　serious　problems　to　the　wireless　sensor　networks　due　to　the　limited　energy.　Data　prediction　method　is　helpful　to　reduce　network　traffic　and　increase　the　network　lifetime　accordingly,　especially　by　exploring　data　correlation　among　the　sensory　data.　Data　prediction　can　also　be　used　to　recover　abnormal/lost　data　in　case　these　sensor　nodes　fail　to　work.　The　current　prediction　methods　in　wireless　sensor　networks　do　not　make　full　usage　of　the　spatial-temporal　correlation　between　wireless　sensor　nodes,　and　thus　leads　to　higher　prediction　error　relatively.　This　paper　proposes　a　novel　model　for　multi-step　sensory　data　prediction　in　wireless　sensor　network.　Firstly,　we　introduce　the　artificial　neural　networks　based　on　1-D　CNN　(One-Dimensional　Convolutional　Neural　Network)　and　Bi-LSTM　(Bidirectional　Long　and　Short-Term　Memory)　to　get　the　abstract　features　of　different　attributes　via　the　pre-processed　sensory　data.　Then,　these　abstract　features　are　used　to　obtain　one-step　prediction.　Finally,　the　multi-step　prediction　is　introduced　by　using　historical　data　and　the　prediction　results　of　the　previous　step　iteratively.　Experiment　results　show　that　after　selecting　suitable　node　combinations　in　which　the　spatial-temporal　correlation　is　highlighted,　the　proposed　multi-step　predictive　model　can　predict　multi-step　(short　and　medium　term)　sensory　data,　and　its　performance　is　better　compared　with　other　related　methods.