Hyper-3DG is a novel method for text-to-3D generation that addresses the challenges of high-order correlations in 3D objects, such as over-smoothness, over-saturation, and the Janus problem. The method, named "3D Gaussian Generation via Hypergraph (Hyper-3DG)", leverages a mainflow and a critical module called "Geometry and Texture Hypergraph Refiner (HGRefiner)" to refine 3D Gaussians. The HGRefiner processes 3D Gaussians by patchifying them into smaller clusters and using hypergraph learning to capture high-order correlations in both spatial and latent visual spaces. This approach enhances the quality of 3D generation without additional computational overhead. The method is validated through extensive experiments, demonstrating superior performance in generating high-fidelity 3D objects with intricate details. The framework is efficient, with low coupling and optimized performance, and is capable of significantly improving the quality of 3D generation without increasing computational load. The results show that Hyper-3DG outperforms existing methods in terms of cross-view consistency, color and texture quality, and structural integrity. Ablation studies confirm the effectiveness of the proposed approach, including the use of ISM loss, 3DGS-Patchify, hypergraph construction, and the selection of hyperparameters. The method also demonstrates the superiority of hypergraph-based models over graph-based ones in capturing high-order correlations in 3D data. The framework is efficient and effective, providing a robust solution for text-to-3D generation.Hyper-3DG is a novel method for text-to-3D generation that addresses the challenges of high-order correlations in 3D objects, such as over-smoothness, over-saturation, and the Janus problem. The method, named "3D Gaussian Generation via Hypergraph (Hyper-3DG)", leverages a mainflow and a critical module called "Geometry and Texture Hypergraph Refiner (HGRefiner)" to refine 3D Gaussians. The HGRefiner processes 3D Gaussians by patchifying them into smaller clusters and using hypergraph learning to capture high-order correlations in both spatial and latent visual spaces. This approach enhances the quality of 3D generation without additional computational overhead. The method is validated through extensive experiments, demonstrating superior performance in generating high-fidelity 3D objects with intricate details. The framework is efficient, with low coupling and optimized performance, and is capable of significantly improving the quality of 3D generation without increasing computational load. The results show that Hyper-3DG outperforms existing methods in terms of cross-view consistency, color and texture quality, and structural integrity. Ablation studies confirm the effectiveness of the proposed approach, including the use of ISM loss, 3DGS-Patchify, hypergraph construction, and the selection of hyperparameters. The method also demonstrates the superiority of hypergraph-based models over graph-based ones in capturing high-order correlations in 3D data. The framework is efficient and effective, providing a robust solution for text-to-3D generation.