Accurately obtaining the motion state of a vehicle is of great significance for the estimation of vehicle trajectory and position, especially in environments with severe satellite signal occlusion (such as urban canyons, wooded areas, tunnels, etc.). However, existing vehicle motion estimation methods driven by dynamic models rely too much on modeling accuracy and are difficult to achieve accurate estimation under some extreme working conditions. Therefore, in order to accurately obtain vehicle motion state information without relying on the accuracy of dynamic models, this paper proposes a hierarchical vehicle state estimation method based on the crested porcupine optimization long short-term memory network. Firstly, by analyzing the basic physical characteristics of vehicle dynamics, a hierarchical estimation strategy for longitudinal velocity and lateral velocity is designed. Then, a dataset composed of various driving conditions is constructed for training, and during the training process, the crested porcupine optimization algorithm is used to optimize parameters such as learning rate and number of iterations. Finally, virtual simulation tests are conducted to verify the estimation method under multiple working conditions and multiple road surface conditions. The results show that this method effectively improves the estimation accuracy. Compared with model-driven methods, the estimation accuracy of lateral velocity, longitudinal velocity, and yaw rate is improved by 92%, 73%, and 52% respectively, and the overall performance is better than the traditional Kalman filtering algorithm based on dynamic models.