  In the area of geographic information processing. There are few researches on
geographic text classification. However, the application of this task in
Chinese is relatively rare. In our work, we intend to implement a method to
extract text containing geographical entities from a large number of network
text. The geographic information in these texts is of great practical
significance to transportation, urban and rural planning, disaster relief and
other fields. We use the method of graph convolutional neural network with
attention mechanism to achieve this function. Graph attention networks is an
improvement of graph convolutional neural networks. Compared with GCN, the
advantage of GAT is that the attention mechanism is proposed to weight the sum
of the characteristics of adjacent nodes. In addition, We construct a Chinese
dataset containing geographical classification from multiple datasets of
Chinese text classification. The Macro-F Score of the geoGAT we used reached
95\% on the new Chinese dataset.
