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Due to the complexity of a train crash, it is a challenging process to describe and estimate mathematically. Although different\ud mathematical models have been developed, it is still difficult to balance the complexity of models and the accuracy of estimation.\ud ,is paper proposes a nonlinear spring-mass-damper model of train frontal crash, which achieves high accuracy and maintains\ud low complexity. ,e Convolutional Long-short-term-memory Generation Adversarial Network (CLGAN) model is applied to\ud study the nonlinear parameters dynamic variation of the key components of a rail vehicle (e.g., the head car, anticlimbing energy\ud absorber, and the coupler buffer devices). Firstly, the nonlinear lumped model of train frontal crash is built, and then the physical\ud parameters are deduced in twenty different cases using D’Alembert’s principle. Secondly, the input/output relationship of the\ud CLGAN model is determined, where the inputs are the nonlinear physical parameters in twenty initial conditions, and the output\ud is the nonlinear relationship between the train crash nonlinear parameters under other initial cases. Finally, the train crash\ud dynamic characteristics are accurately estimated during the train crash processes through the training of the CLGAN model, and\ud then the crash processes under different given conditions can be described effectively. ,e estimation results exhibit good\ud agreement with finite element (FE) simulations and experimental results. Furthermore, the CLGAN model shows great potential\ud in nonlinear estimation, and CLGAN can better describe the variation of nonlinear spring damping compared with the traditional\ud model. ,e nonlinear spring-mass-damper modeling is involved in improving the speed and accuracy of the train crash estimation, as well as being able to offer guidance for structure optimization in the early design stage.