With　the　rapid　advancement　of　machine　learning　technology,　numerous　algorithms　have　been　employed　to　enhance　the　prediction　accuracy　of　material　removal　rate　(MRR).　In　this　paper,　a　novel　prediction　framework　is　introduced　based　on　a　dynamic　hypergraph　attention　mechanism,　providing　accurate　MRR　predictions.　This　approach　utilizes　the　multi-level　attention　mechanism　within　the　hypergraph　architecture　to　effectively　capture　the　relationships　between　nodes　and　hyperedges　in　the　polishing　machine,　enabling　precise　MRR　predictions,　while　maintaining　a　low　data　processing　cost.　Experimental　results　show　that　our　method　achieved　an　RMSE　of　1.42　and　an　R2　of　0.963,　outperforming　current　state-of-the-art　models.　©　2025　IEEE.