To effectively integrate complex features in text and extract different contextual information, this study proposes an inductive text classification method based on a gated graph attention network (TextIGAT). This method constructs a graph structure for each document in the corpus and takes all the words as nodes in the graph to preserve the complete text sequence. One-way connected document-level nodes are designed in the text graph, so that word nodes can interact with global information, and different contextual connection word nodes are merged to introduce more text information in a single text graph. Then, the representations of word nodes are updated utilizing a graph attention network (GAT) and a gated recurrent unit (GRU), and the sequential representation of nodes is enhanced by a bi-directional gated recurrent unit (Bi-GRU) according to the text sequence retained in the graph. TextIGAT can flexibly integrate information from text, which thus allows inductive learning on text with new words and relations. Extensive experiments on four benchmark datasets (MR, Ohsumed, R8, and R52) and detailed analysis prove the effectiveness of our proposed method on text classification.