Graph Convolutional Network (GCN) is an effective tool for classification prediction. In a text classification task, the text is constructed as a word-document graph. However, existing methods only make document category predictions based on document nodes in the word-document graph, neglecting the auxiliary role of word nodes in classification. Based on this, this paper proposes a novel GCN structure based on Token-Task Learning (TTL) for text classification. This paper performs part of speech (POS) tagging or Named Entity Recognition (NER) as auxiliary tasks for text classification. By establishing the relationship between token classification and text classification, text category prediction can take into account the information implied by word nodes, thereby enhancing the accuracy of text classification. In addition, this paper replaces Relu in TextGCN with Mish to enhance data fitting capability of GCN. The experiments are carried out on five text classification datasets, and the experimental results show that the proposed method effectively improves the accuracy of text classification while outperforming the comparison methods.
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