Epilepsy　is　a　common　neurological　disease　characterized　by　recurrent　seizures.　Electroencephalography　(EEG),　which　records　neural　activity,　is　commonly　used　to　diagnose　epilepsy.　This　paper　proposes　an　Empirical　Mode　Decomposition　(EMD)　and　Deep　Convolutional　Neural　Network　epileptic　seizure　prediction　method.　First,　the　original　EEG　signals　are　segmented　using　30s　sliding　windows,　and　the　segmented　EEG　signal　is　decomposed　into　Intrinsic　Mode　Functions　(IMF)　and　residuals.　Then,　the　entropy　features　which　can　better　express　the　signal　are　extracted　from　the　decomposed　components.　Finally,　a　deep　convolutional　neural　network　is　used　to　construct　the　epileptic　seizure　prediction　model.　This　experiment　was　conducted　on　the　CHB-MIT　Scalp　EEG　dataset　to　evaluate　the　performance　of　our　proposed　EMD-CNN　epileptic　EEG　seizure　detection　model.　The　experimental　results　show　that,　compared　with　some　previous　EEG　classification　models,　this　model　is　helpful　to　improving　the　accuracy　of　epileptic　seizure　prediction.