Text classification is a basic research in natural language processing, which aims to output specific label categories for input text. And text representation is the intermediate link of text classification and an important content of text classification. Aiming at the problem that short text has less semantic information and is difficult to represent, this paper proposes a text representation method combines label information and self-attention graph convolutional neural network. This method uses the semantic relation between tags and texts to construct a specific single text representation based on tags, and then extracts the global features of multiple texts by using the self-attention graph convolution neural network to obtain a specific text representation fusing the global features. Finally, the text is input into the classifier to obtain the classification result. The experimental results based on R8 and MR show that compared with the other models, our model increases the F1 values and accuracy by 2.58% and by 2.02% on the MR data; and increases the F1 values and accuracy by 3.52% and by 2.52% on R8 dataset.