This paper proposes a hypergraph neural networks (HGNN) framework for data representation learning, which can encode high-order data correlation in a hypergraph structure. The framework is designed to handle complex data correlations more effectively than traditional graph-based methods. HGNN uses hyperedge convolution operations to efficiently model high-order data correlations, enabling the learning of hidden layer representations that consider the complex data structure. The framework is tested on citation network classification and visual object recognition tasks, where it outperforms graph convolutional networks and other traditional methods. The results show that HGNN is particularly effective in handling multi-modal data. The main contributions of this paper are the proposal of the HGNN framework for representation learning using hypergraph structures and extensive experiments on citation network classification and visual object classification tasks, demonstrating the effectiveness of the proposed HGNN framework. The framework is able to model complex data correlations and is more efficient than traditional hypergraph learning methods due to the use of hyperedge convolution operations. The paper also discusses related work in hypergraph learning and neural networks on graphs, and provides an analysis of the proposed HGNN framework. The experiments show that HGNN achieves better performance on both citation network classification and visual object recognition tasks compared to state-of-the-art methods. The framework is able to handle multi-modal data and complex data correlations, making it a more general and effective approach for data representation learning.This paper proposes a hypergraph neural networks (HGNN) framework for data representation learning, which can encode high-order data correlation in a hypergraph structure. The framework is designed to handle complex data correlations more effectively than traditional graph-based methods. HGNN uses hyperedge convolution operations to efficiently model high-order data correlations, enabling the learning of hidden layer representations that consider the complex data structure. The framework is tested on citation network classification and visual object recognition tasks, where it outperforms graph convolutional networks and other traditional methods. The results show that HGNN is particularly effective in handling multi-modal data. The main contributions of this paper are the proposal of the HGNN framework for representation learning using hypergraph structures and extensive experiments on citation network classification and visual object classification tasks, demonstrating the effectiveness of the proposed HGNN framework. The framework is able to model complex data correlations and is more efficient than traditional hypergraph learning methods due to the use of hyperedge convolution operations. The paper also discusses related work in hypergraph learning and neural networks on graphs, and provides an analysis of the proposed HGNN framework. The experiments show that HGNN achieves better performance on both citation network classification and visual object recognition tasks compared to state-of-the-art methods. The framework is able to handle multi-modal data and complex data correlations, making it a more general and effective approach for data representation learning.