Landslides　caused　by　typhoons　and　rainstorms　frequently　occur　in　the　mountainous　areas　of　southeastern　China,　with　a　complex　mechanism　of　disaster　generation.　Therefore,　establishing　a　prediction　model　for　early　warning　against　such　landslides　is　of　great　theoretical　and　practical　significance.　In　response　to　the　current　shortcomings　in　landslide　displacement　prediction　models,　considering　the　dynamic　evolutionary　characteristics　of　landslide　physical　mechanisms　in　the　study　area,　a　novel　model　based　on　Bayesian　optimization-temporal　convolutional　networks　was　developed.　The　proposed　model　can　automatically　perform　feature　extraction　on　a　dataset　of　complex　multivariate　time　series,　preventing　leakage　of　future　data　during　the　training　process.　Simultaneously,　it　leverages　Bayesian　optimization　to　discover　optimal　hyperparameters　within　the　model,　thereby　offering　additional　insights　into　the　hyperparameter　tuning　process.　Compared　with　recurrent　neural　networks,　the　proposed　model　with　flexible　receptive　fields　has　faster　training　speed　and　parallel　computing　capability.　Finally,　we　experimentally　compared　the　performance　of　the　proposed　algorithm　and　other　common　algorithms　by　analyzing　the　monitoring　data　from　the　Yaoshan　landslide　disaster　in　Anxi　County,　Fujian　Province,　China.　The　results　show　that　the　proposed　model　yields　the　best　prediction　results　in　various　prediction　ranges.