Epilepsy　is　a　common　disease　that　is　caused　by　abnormal　discharge　of　neurons　in　the　brain.　The　most　existing　methods　for　seizure　prediction　rely　on　multi　kinds　of　features.　To　discriminate　pre-ictal　from　inter-ictal　patterns　of　EEG　signals,　a　convolutional　recurrent　neural　network　with　multi-timescale　(MT-CRNN)　is　proposed　for　seizure　prediction.　The　network　model　is　built　to　complement　the　patient-specific　seizure　prediction　approaches.　We　firstly　calculate　the　correlation　coefficients　in　eight　frequency　bands　from　segmented　EEG　to　highlight　the　key　bands　among　different　people.　Then　CNN　is　used　to　extract　features　and　reduce　the　data　dimension,　and　the　output　of　CNN　acts　as　input　of　RNN　to　learn　the　implicit　relationship　of　the　time　series.　Furthermore,　considering　that　EEG　in　different　time　scales　reflect　neuron　activity　in　distinct　scope,　we　combine　three　timescale　segments　of　1 s,　2 s　and　3 s.　Experiments　are　done　to　validate　the　performance　of　the　proposed　model　on　the　dataset　of　CHB-MIT,　and　a　promising　result　of　94.8%　accuracy,　91.7%　sensitivity,　and　97.7%　specificity　are　achieved.　©　2019,　Springer　Nature　Switzerland　AG.