The nonlinearity of soil moisture content, water potential, and unsaturated hydraulic conductivity in soil layers makes it difficult to simulate wetting-drying cycles using conventional means. We addressed this issue using physics-informed neural networks (PINNs). Based on the van Genuchten model, we solved the Richards equation with soil matric potential as the primary variable within the PINNs framework. The proposed approach was applied and validated in a typical deep-buried area at the Luancheng Experimental Station. We found that the PINNs method was as accurate as the finite difference method in simulating the vertical infiltration of soil moisture in a non-laboratory environment. Moreover, the model exhibited swift performance when the soil layer parameters were fine-tuned. Overall, this model accurately characterizes hydrological elements using minimal data, thereby providing a new approach for simulating related hydrological processes.