Abstract: With the advancement of national forestry modernization and the “dual carbon” goal, higher requirements are put forward for the accuracy and efficiency of compiling feasibility study reports for forestry construction projects. The traditional compilation mode has prominent problems such as inefficient data processing, insufficient format standardization, inaccurate policy matching, and subjective risk assessment. Relying on core technologies including natural language processing, multi-modal data fusion, and knowledge graph construction, generative AI, combined with the adaptability advantages of forestry-specific large models, can provide intelligent support in links such as report framework construction, data integration, policy matching, and risk assessment. To address challenges like data security, model reliability, and industry adaptability in applications, optimization measures including strengthening data governance, building trusted technical frameworks, and promoting industry-university-research-government collaborative innovation are needed to provide practical paths for the intelligent upgrading of the forestry engineering consulting industry.