Regional　population　density　has　temporal　and　spatial　characteristics,　and　most　of　the　existing　prediction　models　fail　to　take　these　two　characteristics　into　account　at　the　same　time,　which　results　in　unsatisfactory　forecasting　results.　To　address　this　problem,　we　use　the　deep　learning　models　to　predict　the　crowd　distribution　in　the　evacuation　area,　so　as　to　realize　the　recommendation　of　the　evacuation　area.　First,　a　raster　population　density　prediction　model　based　on　long　short-term　memory　(LSTM)　is　studied,　and　then　a　multiarea　population　density　prediction　model　considering　temporal　and　spatial　characteristics,　named　ST-LSTM,　is　designed.　The　results　of　our　extensive　experiments　on　the　real　dataset　show　that　our　proposed　ST-LSTM　is　both　effective　and　efficient.