Lane-changing　(LC),　which　is　one　of　the　basic　driving　behavior,　largely　impacts　on　traffic　efficiency　and　safety.　Modeling　an　LC　process　is　challenging　due　to　the　complexity　and　uncertainty　of　driving　behavior.　To　address　this　issue,　this　paper　proposes　a　data-driven　LC　model　based　on　deep　learning　models.　Deep　belief　network　(DBN)　and　long　short-term　memory　(LSTM)　neural　network　are　employed　to　model　the　LC　process　that　is　composed　of　LC　decisions　(LCD)　and　LC　implementation　(LCI).　The　empirical　LC　data　provided　by　Next　Generation　Simulation　project　(NGSIM)　is　utilized　to　train　and　test　the　proposed　DBN-based　LCD　model　and　LSTM-based　LCI　model.　The　results　indicate　that　the　proposed　data-driven　model　is　able　to　accurately　predict　the　LC　process　of　a　vehicle.　The　sensitivity　analysis　shows　that　the　most　important　factor　associated　with　LCD　is　the　relative　position　of　the　preceding　vehicle　in　the　target　lane.　This　may　be　the　first　work　that　comprehensively　models　LC　using　deep　learning　approaches.