【Objective】Hypergraph neural networks (HGNN) have the ability to learn inter-class uniqueness and intra-class commonality, which can significantly improve learning performance. However, traditional HGNN methods are in lack of the strong relational induction which determines the way how low-dimensional data nodes interact with each other. In order to solve this problem, a fuzzy HGNN(FHGNN) classification algorithm based on fuzzy theory is proposed, and hypergraph structure is constructed according to the characteristic information of data nodes.【Method】FHGNN first adopts an edge-focused GNN to make edge prediction through iterative updates of edge labels. The fuzzy membership function is designed according to the output of edge prediction to achieve a more accurate representation of the connection relationship between nodes. Finally, the hypergraph is constructed by the relation representation. Then the nodes are classified again and the result is obtained. The edge label loss function and node label loss function are used in FHGNN and their parameters are trained and learned respectively.【Result】Experimental results prove the proposed FHGNN method is more suitable for small-scale data with low node information dimension, and performs well in node classification tasks.【Conclusion】For classification tasks of different data sets, FHGNN can learn the relevant feature information of nodes more effectively and improve the learning effect.