Under the demands for vehicle lightweighting and high safety, friction stir welding has become the core technology for the manufacturing of key vehicle body structures, and it is crucial for the enhancement of collision energy absorption capacity. However, when the traditional explicit dynamic model is used to optimize the process parameters of friction stir welding, a large number of repeated finite element calculations are required, thus leading to such problems as long calculation time and high resource consumption, which restricts the design efficiency. To solve these problems, this paper proposes a time series prediction method of vehicle crash based on the process parameters analysis of friction stir welding, which balances the optimization efficiency and crash safety. During the investigation, first, the mapping relationships between rotational speed, welding speed and the elastic modulus of welded parts are summarized, and a parameter set is constructed. Next, by taking a body-in-white as the object, the set of body-in-white components with mixed shell elements is discretized and the frontal crash condition is set to establish a vehicle explicit dynamic model. Then, a time series prediction surrogate model is designed, and is trained with explicit dynamic response data, with the combination of a high-dimension data decoupler and a penalty function, thus finally forming a surrogate model update process toward the goal of minimizing the deformation and strain of observation points. After iteration, the root mean square error and loss of the surrogate model’s prediction results approach zero, which means that the model accuracy is reliable. In addition, compared with the traditional method, the proposed method saves 50% of the calculation time. This method achieves the collaborative optimization of lightweighting and high safety of vehicles, provides an efficient technical means for vehicle body design and the iteration of friction stir welding process parameters, and has engineering value for shortening the research and development cycle and improving the safety performance of vehicles.