In　order　to　meet　the　demands　of　the　application　scenarios　for　the　fifth-generation　(5G),　such　as　high　transmission　rate　and　low　delay,　the　concept　of　Mobile　Edge　Computing　(MEC)　was　proposed　in　industry.　By　deploying　some　servers　with　computing　and　storage　capabilities　in　mobile　access　networks,　MEC　could　meet　the　above　demands.　However,　there　is　a　problem　of　uneven　resource　utilization　in　edge　data　centers　(EDC)　currently.　Considering　the　mobility-related　factors　of　EDC　to　predict　the　workloads　of　EDC,　the　prediction　results　are　conducive　to　resource　allocation,　so　that　resources　can　be　reasonably　allocated　to　improve　resource　utilization.　In　order　to　perform　accurate　load　prediction,　this　paper　jointly　considers　user　mobility　and　geographic　location　information　of　EDC,　and　proposes　a　method　of　load　prediction　based　on　Long-Short-Term　Memory　Networks　(LSTM).　Experiments　show　that　the　prediction　accuracy　of　the　proposed　method　is　averagely　improved　by　4.21%,　compared　with　the　state-of-the-art　Autoregressive　Integrated　Moving　Average　Model　(ARIMA).　©　2018　IEEE.