Abstract: NO_x emission prediction model of coal-fired power plant can improve denitrification economy. The NO_x emission mechanism is complex, and there are many variables that effect the NO_x emissions. The effective fusion of the information between the correlation variables can improve the NO_x emission prediction accuracy. This paper presented a NO_x emission prediction model through mutual information-graph convolution neural network (MI-GCN). Based on the operation parameters of the 660MW coal-fired power plant, the mutual information between characteristic variables affecting NO_x emission was calculated, the adjacency relationship between characteristic variables was designed, the characteristic adjacency matrix was obtained, and the NO_x emission prediction model based on graph convolution neural network was constructed. The proposed NO_x prediction model was compared with the typical NO_x prediction models based on long short time memory (LSTM), BPNN and least squares support vector machine (LS-SVM). The experimental results show that the MI-GCN prediction model has better generalization ability and higher prediction accuracy