In this paper, a method for driving scene modeling and behavior decision-making based on ontology is proposed to solve the problem that autonomous vehicles have difficulty in effective navigation and decision-making planning on unstructured roads. First, an ontology model of each element in the unstructured road is established, in which the eight-direction model is used to describe the positional relationship between the unmanned vehicle and obstacles in the road scene. Then, the Cartesian coordinate system of the grid map in the autonomous vehicle is converted into the Frenet coordinate system, and the risk function is defined with the combined spring model as the framework to evaluate the risk value of the vehicle driving in the current scene. Then, the photoelectric information data and prior driving knowledge are integrated to form an ontology knowledge base. Finally, the Prolog inference engine is used to infer the final behavior decision result, which must meet the safety and rationality evaluation. Experimental results show that in unstructured roads, this method can give a decision result that is more in line with the driver's behavior at the decision level and also performs well in assisting path planning.