In　this　paper,　we　propose　to　leverage　the　emerging　deep　learning　techniques　for　spatiotemporal　modeling　and　prediction　in　cellular　networks,　based　on　big　system　data.　First,　we　perform　a　preliminary　analysis　for　a　big　dataset　from　China　Mobile,　and　use　traffic　load　as　an　example　to　show　non-zero　temporal　autocorrelation　and　non-zero　spatial　correlation　among　neighboring　Base　Stations　(BSs),　which　motivate　us　to　discover　both　temporal　and　spatial　dependencies　in　our　study.　Then　we　present　a　hybrid　deep　learning　model　for　spatiotemporal　prediction,　which　includes　a　novel　autoencoder-based　deep　model　for　spatial　modeling　and　Long　Short-Term　Memory　units　(LSTMs)　for　temporal　modeling.　The　autoencoder-based　model　consists　of　a　Global　Stacked　AutoEncoder　(GSAE)　and　multiple　Local　SAEs　(LSAEs),　which　can　offer　good　representations　for　input　data,　reduced　model　size,　and　support　for　parallel　and　application-aware　training.　Moreover,　we　present　a　new　algorithm　for　training　the　proposed　spatial　model.　We　conducted　extensive　experiments　to　evaluate　the　performance　of　the　proposed　model　using　the　China　Mobile　dataset.　The　results　show　that　the　proposed　deep　model　significantly　improves　prediction　accuracy　compared　to　two　commonly　used　baseline　methods,　ARIMA　and　SVR.　We　also　present　some　results　to　justify　effectiveness　of　the　autoencoder-based　spatial　model.