A fault diagnosis method based on multi-information weighted fusion and two-dimensional convolutional neural network (MIWF-2DCNN) is proposed to effectively monitor the operating status of hub motors for distributed electric vehicles under complex operating conditions and improve the accuracy of bearing fault identification. Firstly, the multi-directional vibration monitoring signals of in-wheel motor bearing were reconstructed by two-dimensional data reconstruction and time-frequency transformation respectively, and then converted into grayscale images one by one. According to the direction order, the time-domain grayscale atlas and time-frequency domain grayscale atlas were stacked as the input of the fault diagnosis model. Secondly, the network structure of efficient channel attention mechanism (ECANet) was improved, and the improved efficient channel attention mechanism (iECANet) was proposed. The core idea of IECANET was to add a global maximum pooling (GMP) branch on the basis of global average pooling (GAP), and update the weight coefficient of each branch based on the contribution of effective information. Then, the fault features in time domain and time-frequency domain were extracted to realize the weighted fusion of multi-information. Thirdly, GMP was used to simplify a fully connected layer of the traditional two-dimensional convolutional neural network (2DCNN) model to achieve network lightweight. Finally, based on the experimental data of in-wheel motor under different working conditions, the corresponding verification under the same working condition, cross validation under different working conditions and ablation experimental verification were carried out. The results show that the proposed MIWF-2DCNN model can effectively extract the fault features of in-wheel motor bearing, and the fault recognition rate remains above 95% in complex environments and variable working conditions, which is better than the traditional LeNet-5 and 1DCNN models.