To improve the accuracy and robustness of fault diagnosis for metro train bogies, a multi-sensor information fusion diagnosis method combining convolutional neural network (CNN) and an improved graph convolutional network (GCN) is proposed. First, vibration signals collected from multiple sensors are transformed into time-frequency maps using continuous wavelet transform. Then, a five layers CNN is employed to extract features from each channel's time-frequency map, where each sensor is treated as a node in a graph and the extracted features serve as node attributes for the GCN. To overcome the limitation of traditional GCN with fixed adjacency matrices that fail to reflect dynamic relationships among nodes, a multilayer perceptron (MLP) is designed to adaptively update adjacency weights based on node features, enabling dynamic graph convolution. Finally, fault classification is performed through two GCN layers followed by a classification head. Experiments demonstrate that the proposed method achieves an average diagnostic accuracy of 99.46%, and the maximum accuracy can reach 100% under some working conditions, significantly outperforming existing models such as single-channel CNN, CNN-LSTM, CNN-Transformer, MSSCNN. The confusion matrix and t-SNE visualization results show clearly clustered fault features with a misclassification rate below 1%. The study shows that the proposed method maintains excellent diagnostic accuracy and stability under multiple operating conditions and fault modes, providing reliable technical support for the intelligent operation and maintenance of metro bogie systems.