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In order to provide more specific knowledge and technology of plant field, the main task of KG (knowledge graph) is to extract a wealth of concepts and relationships. Due to the relation extraction is the most difficult in KG construction, this paper makes use of ontology learning, and proposes a nontaxonomic relation learning method to obtain representative concepts and their relations from unstructured and semistructured texts of Baidu Encyclopedia entry content by using lexiconsyntactic patterns based on dependency grammar analysis. Moreover, the methods of adding constraint models and words filtering were adopted to build heavy weight ontology automatically based on a lightweight ontology and greatly improved the precision of the relation extraction. The approach established a concept structure from the plant domain corpus, ameliorated the discovery of the most representative non-taxonomic relation, and formalized them in the standardized OWL 2.0. A set of experiments was performed using the approach implemented in the plant domain. The results indicated that extraction by patterns should be performed directly after natural language processing, which has a comparatively high accuracy compared to the former algorithms, and this approach can extract non-taxonomic relations with high effectiveness, which lays the foundation for KG construction of plant field.