Design and Implementation of Simulation Algorithm for New Energy Automobile Industry Based on System Dynamics

Aiming at the problem of insufficient policy incentives for the new-energy economy in Dalian, an optimization model for the industrialization based on system dynamics was proposed and constructed. By introducing dynamic tax rate incentive mechanism as the core strategy, the model was iteratively calculated, and key state variables such as market expansion degree, production capacity growth and tax revenue were studied. The interactive relationship between new energy vehicles and traditional automobile industry in different market expansion stages is simulated, and the specific impact of policy adjustment on industrial development is quantitatively analyzed. The simulation results show that the model not only effectively improves the rapid growth of the new-energy vehicle consumption market in Dalian, but also maintains the level of corporate income tax revenue of the national and local governments, and the financial pressure does not increase significantly. For verifying the predictive function of the algorithm, different algorithms were compared in this study, and the test data express that the LSMN performed best in the prediction data, with MAE of 800, RMSE of 1200, R2 value of 0.90, and training and prediction time of 60 s and 5 s, respectively, which fully demonstrated the potential of the model in dynamic analysis and policy evaluation.