Brain functional hypernetwork is widely used in the classification diagnosis of brain diseases. In the existing studies, the construction of brain functional hypernetwork is extensively studied to improve classification accuracy, but the impact of brain functional hypernetwork topology on classification performance is overlooked. However, it was showed that hyperedge information could make up for the characteristics of hypernetworks, and the transmission between hyperedges is conducive to overall learning. In view of this problem, hyperedge learning of unweighted brain hypernetwork based on line graph was proposed to analyze the influence of hyperedge on the topology and classification performance. Specifically, firstly, based on functional magnetic resonance data, the star extension method was used to construct brain functional hypernetwork. Secondly, line graph theory was introduced to construct the line graph model. Next, hyperedge density was extracted as local feature and nonparametric test was performed to select feature. Then, the graph-based substructure pattern mining algorithm was adopted to extract subgraph and the frequent score feature selection was applied to select discriminate subgraph. Finally, support vector machine was employed to construct classification model. The results show that the classification results of the proposed method are better than those of traditional brain function hypernetwork, reaching 86.79%. It is concluded that the topological information of the brain function hypernetwork affected the construction of the classification model. In addition, the fusion features obtained based on the line graph model are better than any single type of features, reaching 88.68%. It is suggested that that for hyperedge topology information extraction, the attribute information of the hyperedge needs to be considered, and the spatial transfer information ability between hyperedges needs to be considered.