Optimization of New Energy Vehicle Industry Structure Based on Optimization Link Prediction Similarity Algorithm and CGE

Optimization of New Energy Vehicle Industry Structure Based on Optimization Link Prediction Similarity Algorithm and CGE

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In light of the growing global concerns over energy and climate change, new energy vehicles are confronted with both opportunities and challenges posed by industrial structural transformation.To foster the optimization of the new energy vehicle industry structure while considering environmental impact, this paper first uses an improved seattle seahawks socks link prediction algorithm to construct an industrial structure optimization model.Then, a computable general equilibrium model is used to explore the effect of a carbon tax on the new energy vehicle industry.The proposed model had the best area under the curve on the Karate network and FWFW network, which were 90.64% and 65.

05%.In the Jazz network, the prediction accuracy was 92.65%.The prediction accuracy of the proposed model for the “Yangtze River Delta” and “Beijing Tianjin Hebei” regions was 86.37% and 85.

62%.With the gradual rise of carbon tax rates and emission reductions, the demand for electricity, coal, and oil in the new energy vehicle manufacturing industry was gradually decreasing.The gross domestic product and total output of the manufacturing industry were gradually decreasing, and the total investment was gradually increasing.This result demonstrates the application effect of the proposed industrial structure optimization model and indicates that carbon tax collection will have an impact on carbon emissions and energy demand.This research will help promote the optimization of flexcon reverse osmosis water storage tank the new energy vehicle industry structure and promote the sustainable growth of the national economy.