Epilepsy　is　a　chronic　and　recurrent　brain　dysfunction　disease.　An　acute　epileptic　attack　will　interfere　with　a　patient＇s　normal　behavior　and　consciousness,　having　a　great　impact　on　their　life.　The　purpose　of　this　study　was　to　design　a　seizure　prediction　model　to　improve　the　quality　of　patients＇　lives　and　assist　doctors　in　making　diagnostic　decisions.　This　paper　presents　a　transformer-based　seizure　prediction　model.　Firstly,　the　time-frequency　characteristics　of　electroencephalogram　(EEG)　signals　were　extracted　by　short-time　Fourier　transform　(STFT).　Secondly,　a　three　transformer　tower　model　was　used　to　fuse　and　classify　the　features　of　the　EEG　signals.　Finally,　when　combined　with　the　attention　mechanism　of　transformer　networks,　the　EEG　signal　was　processed　as　a　whole,　which　solves　the　problem　of　length　limitations　in　deep　learning　models.　Experiments　were　conducted　with　a　Children＇s　Hospital　Boston　and　the　Massachusetts　Institute　of　Technology　database　to　evaluate　the　performance　of　the　model.　The　experimental　results　show　that,　compared　with　previous　EEG　classification　models,　our　model　can　enhance　the　ability　to　use　time,　frequency,　and　channel　information　from　EEG　signals　to　improve　the　accuracy　of　seizure　prediction.