Abstract: The lightweight bogie is a core technology for achieving ‘weight reduction, energy conservation, quality improvement, and efficiency enhancement’ in high-speed trains. However, the lightweight design alters the dynamic environment of the axlebox bearings, resulting in more complex and concealed fault characteristics. The scarcity of onsite data further restricts the development of intelligent maintenance for axlebox bearings. To address this, this paper proposes a few-shot cross-domain fault diagnosis method assisted by lightweight bogie bearing mechanism simulation. Firstly, a rigid-flexible coupling dynamic model of a lightweight bogie with carbon fiber reinforced plastics (CFRP) side beams is established. Considering the dynamic loads on the bearings from various components, a joint simulation analysis of the bogie-axlebox bearing is conducted to construct a bearing simulation dataset containing typical faults. Secondly, a collaborative model integrating VMamba and cross deformable Transformer (VMCDT) is constructed, combined with a teacher-student network knowledge distillation framework, to achieve in-depth mining and smooth transfer of complex cross-domain features. Finally, by introducing a label smoothing strategy, few-shot fault diagnosis of bearings in cross-domain scenarios is realized. Experimental results from six different testing tasks demonstrate that the proposed method achieves fault diagnosis accuracies exceeding 89% and 93% under 4-way 1-shot and 5-shot conditions respectively, outperforming conventional few-shot learning approaches. This approach overcomes the dual challenges of significant distribution differences between simulation and measured data and the high difficulty of few-shot cross-domain diagnosis, providing a new paradigm for fault diagnosis of axlebox bearings in lightweight bogies of high-speed trains.