To address the widespread instability and volatility in stock price forecasting, as well as the difficulty of parameter optimization in the variational mode decomposition (VMD) algorithm, this paper proposes a two-stage combined prediction framework, CRIME-SE-VMD-VIT2M. In the first stage, the Chebyshev chaos map and lens imaging population selection strategy are introduced on the basis of the original frost ice optimization algorithm. Using SE as the fitness function, an improved CRIME-SE-VMD optimization model is constructed to enhance the global search capability and decomposition quality of parameter optimization. In the second stage, key technical indicators are selected through PCC and fused with the IMFs obtained from VMD decomposition to form a multi-dimensional feature set. Based on this, combined with the optimization results of the first stage, a VIT2M parallel dual-channel prediction model is designed and implemented to deeply extract and model multi-scale stock feature information. Experimental results show that the fitness value of CRIME-SE-VMD on four stock datasets is 0.000 318 9~0.000 703 lower than that of the comparison algorithm, demonstrating better decomposition performance. At the same time, the prediction performance of the VIT2M model on the same datasets is better than that of the comparison model, verifying its effectiveness in improving the accuracy of stock price prediction.