Research on the Application of Knowledge-Enhanced Big Model in Intelligence Data Processing of Energy Think Tanks

This paper proposes an intelligence data processing method based on knowledge-enhanced big model, aiming to improve the energy think tank's ability to analyze multi-source heterogeneous data. By combining domain knowledge graph with big model, this method can effectively integrate domain-specific knowledge and improve the understanding and processing accuracy of energy intelligence. The system design includes knowledge enhancement algorithm and intelligent analysis module, uses multilevel embedding technology to optimize data representation, and improves the generalization ability of the model under different intelligence types through multitask learning. The experiment simulates intelligence data in different situations. The results show that the model performs well in tasks such as energy intelligence information extraction, data fusion and situation prediction, and is significantly better than traditional methods in data accuracy and processing efficiency. Especially in the identification of key intelligence nodes and data trend analysis, the model shows high accuracy and adaptability, which can provide strong support for the decision support of energy think tanks. The simulation results verify the efficiency and reliability of this method in intelligence processing, and prove its broad prospects in energy think tank applications.