In order to solve the problem of a large amount of redundant data in the retrieval process of sheep disease and the waste of resources caused by manual selection of accurate answers after retrieval， this study constructed a question-and-answer system based on the knowledge graph of sheep & goat disease through the following three steps： （1） The data was obtained through web crawlers and some is manually extracted， automated information extraction was carried out using the bidirectional long short-term memory recurrent neural network （Bi-LSTM） model with an attention mechanism for improved recognition efficiency in the named entity recognition task. The entity annotation was performed using the BIO rule to complete the information extraction. The data was then integrated and stored in the Neo4j graph database . （2） For the attribute mapping， we constructed the Bert-softmax model； according to the user’s question， the Bert model was used to calculate the semantic similarity between the question and the attribute to determine the user’s intention， then the softmax algorithm was used to for normalization， finally， the most suitable answer was found and fed back to the system. （3） We built a sheep & goat disease diagnosis platform using Bootstrap， Echarts， and Vue components to visualize the sheep disease question-and-answer system.We used flask framework included in the Python language to build a backend， encapsulate disease information， present it to users through the web frontend， and establish a connection on the backend to enable data interaction. The results in the study show that the F₁ value of entity recognition based on Bi-LSTM + Attention + CRF model is 83.16%， and the constructed knowledge graph contains 4 576 entities and more than 13 000 entity relationships. The pre-trained model Bert was added to the question answering system， and the F₁ value of problem recognition was 85.24%. The results indicated that the system can quickly retrieve and accurately answer various types of questions such as the prevention and control measures of sheep diseases， and assist the farmers to make production decisions when faced with sheep diseases.