The　pedestrian　trajectory　prediction　forecasts　future　positions　by　analyzing　historical　data　and　environmental　context.　With　the　rapid　advancement　of　artificial　intelligence　and　data　processing　technologies,　this　technique　has　become　increasingly　significant　in　areas　such　as　autonomous　driving,　video　surveillance,　and　intelligent　transportation　systems.　Traditional　deep　learning　methods　have　primarily　focused　on　time-domain　modeling　and　have　made　great　success.　However,　they　struggle　to　capture　multi-scale　features　and　frequency-domain　information　in　trajectories,　making　it　challenging　to　effectively　handle　noise　and　uncertainty　in　trajectory　data.　To　address　these　limitations,　this　paper　proposes　a　Multi-Scale　Wavelet　Transform　Enhanced　Graph　Neural　Network　(MSWTE-GNN)　based　on　wavelet　transform　and　multi-scale　learning.　The　model　processes　trajectory　sequences　in　the　frequency　domain　using　wavelet　transform,　extracting　multi-scale　features,　and　integrates　multi-scale　graph　neural　networks　with　cross-scale　fusion　to　learn　interaction　information　among　pedestrians.　Experimental　results　demonstrate　that　the　proposed　method　significantly　improves　the　accuracy　and　reliability　of　pedestrian　trajectory　prediction.