The　highway　slope　failures　are　triggered　by　the　rainfall,　namely,　to　create　the　disaster.　However,　forecasting　the　failure　of　highway　slop　is　difficult　because　of　nonlinear　time　dependency　and　seasonal　effects,　which　affect　the　slope　displacements.　Starting　from　the　artificial　neural　networks　(ANNs)　since　the　mid-1990s,　an　effective　means　is　suggested　to　judge　the　stability　of　slope　by　forecasting　the　slope　displacement　in　the　future　based　on　the　monitoring　data.　In　order　to　solve　the　problem　of　forecasting　the　highway　slope　displacement,　a　displacement　time　series　forecasting　model　of　cohesionless　soil　highway　slope　is　given　firstly,　and　then　modular　neural　network　(MNN)　is　used　to　train　it.　With　the　randomness　of　rainfall　information,　the　membership　function　based　on　distance　measurement　is　constructed;　after　that,　a　fuzzy　discrimination　method　to　sample　data　is　adopted　to　realize　online　subnets　selection　to　improve　the　self-adapting　ability　of　artificial　neural　networks　(ANNs).　The　experiment　on　the　sample　data　of　Beijing　city＇s　highway　slope　demonstrates　that　this　model　is　superior　to　others　in　accuracy　and　adaptability.