A B/C e-commerce management model integrated with a Label Propagation Algorithm (LPA) is developed to enhance the management efficiency and development potential of new energy enterprises. Enterprise databases and network monitoring platforms are employed to collect operational data, which are then labeled and classified using LPA. Wind, hydropower and solar energy data are processed within the B/C framework to enable comprehensive comparison, mapping and logical association. The proposed model supports unified data integration, correlation analysis and predictive decision-making for renewable energy management. Experimental results indicate that the e-commerce platform achieves a data integration rate of 92% across multiple energy sources and a prediction accuracy of 82% for future development trends. In addition, the structural optimization rate of new energy development reaches 90.6%. The results show that the B/C-LPA framework improves energy allocation, reduces waste and supports sustainable energy management.
Bai Haiyan (Sun,) studied this question.