Precipitation　nowcasting　within　2　h　is　an　important　and　hard　issue　in　the　weather　research　area.　Benefiting　from　the　outstanding　nonlinear　relationship　modeling　capability,　methods　based　on　deep　learning　(DL)　have　achieved　significant　success　in　the　task　of　precipitation　nowcasting　compared　to　the　others.　However,　existing　DL-based　methods　always　disregard　the　intricate　high-order　correlations　and　lack　substantial　connections　with　the　evolution　of　the　precipitation　system,　which　would　lead　to　blurred　forecasts　and　implausible　predictions.　To　address　these　issues,　we　proposed　a　new　Precipitation　Nowcasting　Network　within　a　2-h　model　based　on　the　Hypergraph　Neural　Network　(PN-HGNN).　In　this　work,　a　Hypergraph　Neural　Network　is　first　adopted　for　extracting　spatiotemporal　dynamic　echo　features.　Second,　regulation　evolution　is　in　charge　of　capturing　the　memory　features　to　guide　the　extrapolation.　Finally,　we　design　a　dual　branch　module　to　extrapolate　the　radar　echoes.　The　proposed　model　has　been　assessed　on　the　dataset　HKO-7.　The　experimental　results　demonstrate　that　PN-HGNN　achieved　better　prediction　performance　than　the　six　representative　echo　extrapolation　models.