The previous deep learning fault diagnosis methods for transmission lines rely on digital signal processing technology to extract fault features. In order to improve the above methods, this paper introduces graph deep learning theory and proposes an endtoend intelligent fault diagnosis method based on graph attention network. The original threephase current and voltage signals are converted into graph data, and the feature is automatically extracted using multiple graph attention layers, thus establishing the mapping relationship between the data from input to output, and realizing endtoend fault diagnosis of transmission lines. The accuracy and effectiveness of the method are verified on the 400 kV threephase transmission line and the IEEE13 bus power grid system, and the simulation analysis is carried out for five kinds of short circuit fault and no fault conditions with different initial phase angle, transition resistance and fault location. The results show that the fault diagnosis accuracy of this method is more than 99.75%, and its performance is the best compared with several existing intelligent fault diagnosis algorithms. At the same time, the method still maintains high fault identification rate under different white noise, has good robustness and generalization ability, and provides a certain research idea for power transmission line diagnosis technology.