The integration of multi-source heterogeneous agricultural information not only help understanding and unveiling the pathways of diseases infection and prevention but also offers vital data support for research on diseases prescription recommendations. Aiming at the problems of fusion, alignment and heterogeneity in the analysis of multi-source heterogeneous agricultural data such as electronic medical records, the knowledge graph of crop diseases prescription was constructed, and the diseases association on this basis was visually analyzed. Initially, starting from the principle of the disease triangle, the critical roles of pathogens, hosts, and environment in the process of disease infection and control were analyzed, constructing the ontology layer of the crop diseases prescription knowledge graph with 18 ontology concepts, 17 relationships, and 6 attribute edges based on data characteristics. Subsequently, the entity layer of the knowledge graph was built by integrating rule-matching and deep-learning knowledge extraction methods, encompassing 1 121 entity instances and 8 292 relationship instances. Lastly, identification of Top20 key nodes based on degree and betweenness centrality, along with Top5 disease-prevention product association predictions using the Adamic-Adar index, were conducted to visually analyze the associations between key entities and attributes, including diseases, symptoms, and prevention products. Six rules were established to enhance the recommendation and information retrieval functions of disease prevention products from three perspectives: prevention scheme selection, green subsidy filtering, and related information inquiry. The research result can provide reference for electronic medical record data mining and association analysis of crop diseases.