Weather　forecasting　is　inextricably　linked　to　human　lives　and　represents　a　quintessential　task　of　spatiotemporal　modeling,　necessitated　by　the　spatial　and　temporal　dependencies　inherent　in　meteorological　data.　Recent　studies　have　consistently　shown　the　excellent　performance　of　graph-based　neural　networks　in　accurately　modeling　spatiotemporal　data　across　various　applications.　Yet,　traditional　graph　neural　networks　(GNNs)　are　unable　to　handle　the　high-order　diffusion　and　aggregation　phenomena　between　meteorological　data　caused　by　advection.　Moreover,　the　impacts　of　spatial　correlation　among　multisource　information　and　the　presence　of　noise　in　meteorological　data　are　often　overlooked.　This　study　proposes　a　novel　approach　for　modeling　the　spatiotemporal　dependencies　in　meteorological　data　using　the　multi-information　spatiotemporal　aggregation　and　estrangement　hypergraph　convolution　network.　This　method　employs　a　novel　representation　of　meteorological　data　using　hypergraphs　to　address　the　aforementioned　challenges.　Specifically,　we　construct　adjacency　and　semantic　hypergraphs　to　represent　spatial　correlations　and　then　introduce　aggregation　and　estrangement　hypergraph　convolution　networks　to　effectively　capture　multi-information　spatial　correlations.　A　new　reconstruction　feature　attention　module　has　been　developed　to　fuse　aggregation　and　estrangement　semantic　spatial　information　across　various　subspaces.　In　addition,　the　hypergraph　convolution　is　embedded　within　a　recurrent　neural　network　architecture　to　model　the　temporal　correlations.　Extensive　experiments　have　been　conducted　on　four　weather　datasets,　and　state-of-the-art　performance　has　been　achieved　in　comparison　to　mainstream　baseline　methods.