Because　of　the　burstiness　and　uncertainty　of　network,　the　prediction　for　short-term　network　traffic　is　a　difficult　problem.　This　paper　proposes　a　real-time　network　traffic　prediction　model　based　on　Long　Short-Term　Memory　(LSTM)　neural　network.　The　loss　function　of　LSTM　network　is　modified　to　enhance　the　robustness　of　the　prediction　model.　Different　from　the　traditional　LSTM　model,　the　proposed　model　is　continually　updated　with　the　arrival　of　new　traffic.　The　experimental　results　show　that　the　proposed　model　performs　better　on　prediction　accuracy　than　other　models　constructed　with　Support　Vector　Regression　and　Back　Propagation　neural　network.　©　2018　IEEE.