Green Energy Consumption Path Selection and Optimization Algorithms in the Era of Low Carbon and Environmental Protection Digital Trade

In order to better study the chosen path of the consumption model of public green energy and more accurately predict consumers’ green energy consumer behavior, we take new energy vehicles as an example to explore the driving mechanism and internal mechanism of the public green energy consumption model from the perspective of motivation. We propose an ensemble learning model based on a stacking strategy. The model uses XGBoost, random forest and gradient lifting decision trees as primary learners to transform features, and uses logistic regression as a meta-learner to predict users’ consumer behavior. The experimental results show that this feature engineering method can significantly improve the accuracy rate in multiple model algorithms, and the prediction effect of the ensemble learning model is better than that of a single model, with the accuracy rate of 82.81%. In conclusion, the ensemble learning model based on a stacking strategy can effectively predict the public’s consumer behavior. This provides a theoretical basis and policy recommendations for promoting green energy products represented by new energy vehicles, thereby improving the practical path for proposing green energy consumption.