In this study, we propose a subspace-enhanced hypergraph neural network (seHGNN) for classifying anxiety disorders (AD), which are prevalent mental illnesses that affect a significant portion of the global population. Our seHGNN model utilizes a learnable incidence matrix to strengthen the influence of hyperedges in graphs and enhance the feature extraction performance of hypergraph neural networks (HGNNs). Then, we integrate multimodal data on the brain limbic system into a hypergraph within an existing binary hypothesis testing framework. Experimental results demonstrate that our seHGNN achieves a remarkable accuracy of 84.46% for AD classification. By employing an ensemble learning strategy, we can further improve its performance, achieving a high accuracy of 94.1%. Our method outperforms other deep-learning-based methods, particularly GNN-based methods. Furthermore, our seHGNN successfully identifies discriminative AD biomarkers that align with existing reports, providing strong evidence supporting the effectiveness and interpretability of our proposed method.