To enhance the efficiency of acquiring knowledge on swine disease prevention and control and to alleviate the shortage of veterinary service resources at the grassroots level，this study develops a vertical domain knowledge graph and an intelligent question-answering system specifically for swine disease prevention and control.To resolve descriptive conflicts in multi-source heterogeneous data，the study establishes a domain ontology based on veterinary pathology and introduces a BERT-based semantic similarity fusion strategy to construct a professionally annotated corpus.Given the complex and long-tail distribution of veterinary terms，we propose an ERNIE-Bi-LSTM-CRF entity recognition model.Named entity recognition experiments and ablation tests show that ERNIE’s unique entity-level masking mechanism effectively overcomes the semantic fragmentation problem caused by traditional character-level masking of complex terms.The F₁ score reaches 89.63%，significantly outperforming baseline models such as Bi-LSTM+CRF and BERT.Based on this，the study integrates the Neo4j graph database and intent recognition technology to develop a WeChat-based question-answering chatbot.Tests show that the chatbot can accurately understand professional veterinary diagnosis and treatment issues，providing comprehensive，and scientifically sound diagnostic guidance.This effectively assists farmers in disease diagnosis.The results indicate that the developed system can achieve a closed-loop process，from natural language queries to knowledge retrieval within the knowledge graph，thereby offering technical support for swine disease diagnosis and prevention knowledge services.