Epilepsy　is　a　chronic　brain　disease　caused　by　excessive　discharge　of　brain　neurons.　Long-term　recurrent　seizures　bring　a　lot　of　trouble　to　patients　and　their　families.　Prediction　of　different　stages　of　epilepsy　is　of　great　significance.　We　extract　pearson　correlation　coefficients　(PCC)　between　channels　in　different　frequency　bands　as　features　of　EEG　signals　for　epilepsy　stages　prediction.　However,　the　features　are　of　large　feature　dimension　and　serious　multi-collinearity.　To　eliminate　these　adverse　influence,　the　combination　of　traditional　dimension　reduction　method　principal　component　analysis　(PCA)　and　logistic　regression　method　with　regularization　term　is　proposed　to　avoid　over-fitting　and　achieve　the　feature　sparsity.　The　experiments　are　conducted　on　the　widely　used　CHB-MIT　dataset　using　different　regularization　terms　L1　and　L2,　respectively.　The　proposed　method　identifies　various　stages　of　epilepsy　quickly　and　efficiently,　and　it　presents　the　best　average　accuracy　of　94.86%,　average　precision　of　96.71%,　average　recall　of　93.48%,　average　kappa　value　of　0.90　and　average　Matthews　correlation　coefficient　(MCC)　value　of　0.90　for　all　patients.