To achieve fault type judgment, fault line localization, and fault distance judgment in the grid, a novel hybrid network model integrating fast Fourier transform (FFT), convolutional neural networks (CNN), and graph convolutional networks (GCN) was proposed for grid fault diagnosis. Voltage and current signals were decomposed in time and frequency domains by FFT to extract fundamental waveform amplitude and phase. CNN extracted the temporal features of the decomposed data, and layer normalization was introduced to enhance the model stability. The spatial topology of the grid was processed with GCN to extract and integrate spatial features. The model’s effectiveness was verified through modeling and simulation of the IEEE 39-bus power grid system. Experimental results show that the proposed model possesses strong generalization capabilities, and the fault diagnosis accuracy under various tasks, sampling intervals, and noise conditions outperforms existing models.