Two　seizure　prediction　models　are　built　based　on　a　decision　fusion　strategy　and　residual　network　by　using　spatial　coupling　features　and　introducing　an　attention　mechanism.　First,　eight　frequency　bands　are　filtered,　and　the　correlation　matrices　are　computed　for　each　frequency　of　eighteen　channels.　Second,　the　eight　18x18　matrices　are　input　to　the　residual　module　for　classification,　and　the　results　are　concatenated　to　form　a　vector.　A　fully　connected　layer　is　used　for　decision　fusion.　Third,　to　emphasize　the　coupling　relationship　among　the　different　frequency　bands,　a　cubic　matrix　formed　by　the　eight　18x18　matrices　is　inputted　to　an　attention　network,　resulting　in　the　enhanced　features.　A　seizure　prediction　model　is　thus　proposed　by　combining　the　nine　decisions.　The　performance　of　the　model　is　compared　with　those　from　state-of-the-art　methods,　and　the　sensitivity　of　the　proposed　model　is　improved　by　4.45%.