The　study　on　forecasting　demand　for　online　car-hailing　holds　substantial　implications　for　both　online　car-hailing　platforms　and　government　agencies　responsible　for　traffic　management.　This　research　proposes　an　enhanced　Empirical　Mode　Decomposition　Long-short　Term　Memory　Neural　Network　(EMD-LSTM)　model.　EMD　technique　reduces　noise　and　extracts　stable　intrinsic　mode　functions　(IMF)　from　the　original　time　series.　Genetic　algorithm　is　deployed　to　improve　the　K-Means　clustering　for　determining　optimal　clusters.　These　sub　time　series　serve　as　input　for　the　prediction　model,　with　combined　results　giving　final　predictions.　Experimental　data　from　Didi　includes　Haikou＇s　car-hailing　orders　from　May　to　October　2017　and　Beijing＇s　from　January　to　May　2020.　Results　show　improved　EMD-LSTM　reduces　instability　and　captures　characteristics　better.　Compared　to　unmodified　EMD-LSTM,　RMSE　decreases　by　3.50%,　6.81%,　and　6.81%　for　the　three　datasets,　and　by　30.97%,　20%,　and　9.24%　respectively　compared　to　single　LSTM　model.