Since multilabel text classification datasets often face the problem of label imbalance, therefore, using either sequence-based deep learning (DL) model or graph neural network (GNN)-based DL model alone will not achieve satisfactory classification results. To solve the above problem, firstly, two coattention networks are constructed to simultaneously obtain the sequence-based and GNN-based eigenvectors. Second, labels are added to the graph as global features, and a graph data augmentation strategy is proposed. When obtaining GNN-based eigenvectors, at first, connection and attention weights are obtained through adjacency matrix and the attention of neighborhoods. Then, node features are updated based on convolution and multihead attention, respectively. Multiple comparison experiments on four benchmark datasets prove that the model constructed in this article achieves the optimal classification results and can solve the label imbalance problem.

Although graph neural networks based methods can solve the uneven text length problem of text classification datasets, they are difficult to address the data sparsity problem of short texts. Although some researchers try to reduce the sparsity of the graph by adding labels to its structure, most of them only treat labels as node features other than words and documents, which is not sufficient to construct denser matrices. To address the above problems, three label data augmentation strategies are proposed to build a dense graph, and the attention mechanisms are used to update node features. In addition, a node feature updating method that simultaneously uses global and local weights is proposed. Multiple comparative experiments on five benchmark datasets demonstrate that the method proposed in this article is optimal and the accuracy and micro-F1 have improved by at least 0.012 on four benchmark datasets.