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Diseases in aquaculture are an important factor affecting the efficiency of aquaculture. Due to the disorderly text data of aquaculture diseases, it is difficult to quickly and accurately locate the causes of diseases, which delays diagnosis and treatment, leading to a decrease in the quality and yield of aquaculture. To solve the above problems, we delve into the working principles and model features of knowledge graphs, use knowledge graph technology to complete the overall design of aquaculture disease diagnosis, establish a corpus of aquaculture diseases, introduce the H-BIO annotation strategy, complete the annotation scheme design, improve the BiLSTM model construction, extract entity relationships and train aquaculture disease models, complete the visualization design of aquaculture disease knowledge graphs, and conduct experiments on joint extraction of aquaculture diseases. The experimental results show that the improved BiLSTM model based on knowledge graph has good performance and high reliability in entity relationship extraction, effectively improving the accuracy of joint extraction of aquatic diseases. A visual knowledge graph of aquatic disease has been constructed, which can assist operators in quickly and accurately diagnosing and treating aquatic diseases. It plays a very important role in improving the production efficiency of aquaculture.