In order to construct a rice fertilizer knowledge structure, based on the existing rice fertilizer unstructured data information, a rice fertilizer knowledge graph entity and relationship knowledge structure was proposed and designed, through which the existing rice fertilizer information in the network was stored in the knowledge graph as structured data;in order to extract a large amount of information to be stored in the knowledge graph, and at the same time, for the information extraction i.e., the existence of the overlapping triad problem, a rice fertilizer information extraction model based on RoBERTa-wwm coding + improved CASREL decoding was proposed, and the model was improved according to the characteristics of rice fertilizer data, and relevant experimental comparisons were conducted in coding and decoding, respectively. The results showed that the F1 value of this rice fertilizer information extraction model reached 91.86%, which was a significant improvement in extraction effect compared with the comparison model. Therefore, it can be concluded that the information extraction model based on the improved RoBERTa-wwm-CASERL can effectively improve the extraction effect of rice fertilizer information, which provided a basis for the next step of constructing rice fertilizer knowledge map and rice fertilizer decision system.