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With its powerful data processing ability and convenient end-to-end characteristics, fault diagnosis method based on deep neural network (DNN) has been widely used in many professional fields. However, the current network models mostly are trained based on the measurement information, and few make use of the prior knowledge of the system of interest. Therefore, a new fault diagnosis method based on an improved graph convolution network (GCN) is proposed. Specifically, this method uses the prior knowledge to construct the structural analysis (SA) method, then the SA is used to pre-diagnose the faults and construct the association graph. Next, the association graph and measurements are sent into the improved GCN model to train the network model iteratively, in which a weight coefficient ( $\theta$ ) is proposed to adjust the influence of measurements and the prior knowledge. A particle swarm optimization algorithm (PSO) is used to find the optimal $\theta$ . Finally the fault diagnosis is realized by trained GCN model. This method makes comprehensive use of measurement information and prior knowledge, and achieves better results than other existing fault diagnosis methods in the experiment. Especially, to achieve the same level of diagnosis performance, much less samples are needed in the proposed method when comparing with the state-of-the-art DNN methods.