To　further　reduce　the　error　rate　of　rainfall　prediction,　we　used　a　new　machine　learning　model　for　rainfall　prediction　and　new　feature　engineering　methods,　and　combined　the　satellite　system＇s　method　of　observing　rainfall　with　the　machine　learning　prediction.　Based　on　multivariate　correlations　among　meteorological　information,　this　study　proposes　a　rainfall　forecast　model　based　on　the　Attentive　Interpretable　Tabular　Learning　neural　network　(TabNet).　This　study　used　self-supervised　learning　to　help　the　TabNet　model　speed　up　convergence　and　maintain　stability.　We　also　used　feature　engineering　methods　to　alleviate　the　uncertainty　caused　by　seasonal　changes　in　rainfall　forecasts.　The　experiment　used　5　years　of　meteorological　data　from　26　stations　in　the　Beijing-Tianjin-Hebei　region　of　China　to　verify　the　proposed　rainfall　forecast　model.　The　comparative　experiment　proved　that　our　proposed　method　improves　the　performance　of　the　model,　and　that　the　basic　model　used　is　also　superior　to　other　traditional　models.　This　research　provides　a　high-performance　method　for　rainfall　prediction　and　provides　a　reference　for　similar　data-mining　tasks.