To solve the problems of complex frequency information, strong time variation and obvious modulation characteristics of planetary gearbox vibration signal, a fault diagnosis method of planetary gearbox based on visual spectral feature fusion was proposed. Initially, Welch's transformation is applied to planetary gearbox signals to obtain power spectra. Subsequently, a visual graph algorithm is used to construct a graph spectrum, and centrality measures of the graph nodes are calculated to form a feature matrix. Finally, an improved CNN-Inception model is employed to obtain the fault diagnosis results of the planetary gearbox. Experimental results demonstrate that this method can accurately identify faults in planetary gearboxes. In the experimental datasets covering two operational conditions, the model achieves an accuracy of 98.57%, demonstrating its generalization ability. Compared with alternative methods, the proposed approach exhibits higher accuracy and stronger generalization capabilities.