Abstract: The advancement of shrimp farming is generally hampered by limited technical expertise among practitioners, a shortage of domain specialists and local experts with hands-on experience, and delayed technical guidance, leading to managerial inefficiency and elevated operational risks. To fill this gap, an intelligent question-answering model that integrates knowledge graph with user intent recognition is proposed, aiming to provide scientific and efficient decision-making support for shrimp farming. The proposed model systematically consolidates empirical knowledge from local experts and experimental data from domain specialists to construct a vertical knowledge graph, which encompasses critical aspects of the shrimp farming process, including environmental water quality control, cultivation management, and disease prevention. On this basis, a dual-channel query preprocessing framework is designed, combining intent recognition with semantic enhancement. This designed framework utilizes intent recognition technique to accurately direct ambiguous user queries to pertinent sub-graphs, thereby improving retrieval precision. Meanwhile, it leverages parallel semantic enhancement technique to enrich query context, increasing the information density of sparse queries and activating potential entities and relationships within the knowledge graph. Additionally, community-level graph retrieval is adopted to generate well-structured and context-aware answers. Experimental results show that, the proposed model outperforms four baseline models, in terms of answer completeness and faithfulness. On factual questions, it achieves a 20.73 percentage points improvement in completeness compared with standard retrieval-augmented generation models. On causal questions, it elevates faithfulness by 13.97 percentage points over simple hybrid retrieval models. Ablation studies show that, the synergy between intent recognition and semantic enhancement is critical to the performance of the proposed model. Furthermore, domain adaptation analysis demonstrates that the proposed model maintains stable performance across diverse farming scenarios.