In order to explore the application and implementation of knowledge mapping technology in intelligent agricultural production and realize the accurate query and visualization of complex and diverse agricultural production data, this study took wheat varieties as an example and collected the information of 1852 wheat varieties, 735 micro encyclopedias, and 102 349 entries by a crawler. Through knowledge mapping technology, this study designed the entities of variety knowledge graphs and their relationships, with data cleaned, extracted, and fused. A total of 258 484 entities were recognized and 328 933 relationships built. On this basis, the approach to storing wheat variety knowledge was worked out, with structured data stored in a MySQL, unstructured data in the MongoDB. Neo4j was employed to optimize knowledge query. In this way, the query about relationships between wheat varieties and entity recognition was made possible with variety data expressed accurately and visualized, proving the feasibility of knowledge mapping in visualization of information such as variety. This research can provide technical reference and theoretical support for the application of knowledge mapping in agriculture.