Abstract: Text classification is a basic and important task in natural language processing,and graph neural networks have been applied to this task in recent years.However,the graph representation learning using graph neural networks can not well satisfy the generalization learning of new words in the task involving text classification.It is generally assumed that training and testing data come from the same distribution,which is often invalid in reality.To overcome these problems,this paper puts forward the Invariant-GCN,which is used for text categorization by GCN reported.First,to build a single figure for each document,use GCN to learn fine-grained word representation according to its local structure,which can effectivelygenerate embeddings for words not seen in the new document and then merge the word nodes as document embeddings.And then extract the maximum limit retained within the same class information expectations subgraph,use the graph to study is not affected by the distribution change.Finally,the text classification is completed by graph classification method.In four benchmark datasets,the the Invariant-GCN is compared with five classification methods,and the experimental results show that it has a good effect of text categorization.

Key words: Text classification, Graph convolutional neural network, Casual learning, Text graph construction