Soil zoology is undergoing a period of development with a knowledge system at its core and data-driven. The current widely used data processing and analysis methods based on databases are facing the contradiction between the rapid growth of data and the lack of data processing capacity. Data mining methods based on rapidly developing big data science and artificial intelligence techniques have the outstanding advantages in solving the aforementioned contradictions, but they need to rely on a strong domain knowledge base. Yet there is a paucity of research on knowledge graph in the soil animal domain. The soil animal knowledge graph is a knowledge base with the directed graph structure, where the nodes of the graph represent entities or concepts related to soil animals, and the edges of the graph represent various semantic relationships between the entities or concepts. This paper introduced the concept, connotation and construction method of the soil animal knowledge graph. The soil animal knowledge graph is composed of triples (entity, relation, entity) to describe the relationships among entities. Take the soil mite diversity across an altitudinal gradient in Tianmu Mountain, Zhejiang Province as an example, this paper provided the method and process of constructing a mountain soil animal knowledge graph. In the process of constructing a soil animal knowledge graph, the data source was identified firstly, and the ontology construction objective and process of the soil animal knowledge graph were designed. The process of ontology construction included designing the soil animal knowledge graph model, dividing the core classes and class hierarchies, defining ontology attributes, and evaluating the ontologies. Finally, a preliminary graph of the mountain soil animal knowledge graph was displayed with Neo4j platform. To show the process of a data mining for a soil animal knowledge graph, this paper answers three important scientific questions based on the constructed mountain soil animal knowledge graph:that is where are the soil animals distributed, what species live together, and how environmental conditions affect the soil animal distribution. Finally, this paper pointed out that the soil animal knowledge graph has friendly portability and excellent scalability and indicated that some important scientific issues would be solved quantitatively for soil zoology in the future. In total, this study shows that the soil animal knowledge graph has unique potential and advantages in addressing importantly scientific questions about biodiversity, and has promoted the development of soil animal informatics at the intersection of soil zoology, information science and data science.