Abstract: With the continuous development of the large language model， it has been widely applied in many fields. Due to the lack of knowledge in the dual carbon field in the big language model， the accuracy of the response results is low if the large language model is directly applied to the field of double carbon. Therefore， the method of constructing dual carbon knowledge graph as a knowledge base is adopted to enhance the application of large language models in the field of carbon peaking and carbon neutrality. The LoRA method is used to fine-tune the large language model to improve its ability to extract keywords in the carbon peaking and carbon neutrality fields. A dual carbon knowledge graph is constructed as local knowledge base to provide dual carbon domain knowledge for the model. The knowledge is used as the context of the problem， allowing the large language model to learn， and a prompt engineering assistance model is designed to generate responses. Finally， the effectiveness of the responses is evaluated. The experimental results show that， compared with the direct use of large language model， the method based on knowledge graph to enhance the dual carbon domain service of large language model has a high accuracy of intelligent response results in the field of carbon peaking and carbon neutrality， and provides an effective assistance for the construction of carbon peaking and carbon neutrality.

Key words: large language model, knowledge graph, knowledge base, LoRA method, peak carbon dioxide emissions and carbon neutrality