The　data　collected　by　the　wireless　sensor　nodes　often　has　some　spatial　or　temporal　redundancy,　and　the　redundant　data　impose　unnecessary　burdens　on　both　the　nodes　and　networks.　Data　prediction　is　helpful　to　improve　data　quality　and　reduce　the　unnecessary　data　transmission.　However,　the　current　data　prediction　methods　of　wireless　sensor　networks　seldom　consider　how　to　utilize　the　spatial-temporal　correlation　among　the　sensory　data.　This　paper　has　proposed　a　new　data　prediction　method　multi-node　multi-feature　(MNMF)　based　on　bidirectional　long　short-term　memory　(LSTM)　network.　Firstly,　the　data　quality　is　improved　by　quartile　method　and　wavelet　threshold　denoising.　Then,　the　bidirectional　LSTM　network　is　used　to　extract　and　learn　the　abstract　features　of　sensory　data.　Finally,　the　abstract　features　are　used　in　the　data　prediction　by　adopting　the　merge　layer　of　the　neural　network.　The　experimental　results　show　that　the　proposed　MNMF　model　has　better　performance　compared　with　the　other　methods　in　many　evaluation　indicators.