In order to address the issues of long design cycles， high design complexity， and excessive reliance on engineer experience in traditional lightweight design of tractor chassis， an intelligent lightweight design method is proposed. Firstly chassis performance data is obtained through Design of Experiments （DOE） combined simulation. Then， the BS-TabNet model is constructed based on the TabNet algorithm， Bayesian optimization algorithm， and SHapley Additive exPlanation （SHAP） theory. This model is used to learn from the chassis performance data and generate a surrogate model for the chassis. Finally， the Levy flight strategy is applied to improve the Sparrow Search Algorithm （SSA）， resulting in the Levy Sparrow Search Algorithm （LSSA）， which is used to solve the lightweight design task and find the optimal structural parameters for the chassis. Compared with traditional machine learning algorithms， the BS-TabNet model shows higher accuracy， stability， and interpretability. Its accuracy reaches around 0.98， stability is improved by over 50%， and it has stronger interpretability， addressing the poor performance of deep learning on tabular data. Compared with traditional swarm intelligence optimization algorithms， the LSSA algorithm can obtain better optimization results. While meeting other performance requirements， it achieves a 5.64% reduction in chassis weight. The intelligent lightweight design method combines artificial intelligence with chassis lightweight design， and it can save a significant amount of design time and improve design efficiency.