Currently, network topology becomes increasingly complex with the increased number of various network nodes, bringing in the challenge of network design and analysis. Most of the current studies are deduced based on the binary system stochastic geometry, overlooking the coupling and collaboration among nodes. This limitation makes it difficult to accurately analyze network systems, such as reconfigurable intelligent surface (RIS) assisted ultra-dense network (UDN). To address this issue, we propose a dual coordinate system analysis method, by using dual observation points and their established coordinates. The concept of a typical triangle that consists of a base station (BS), a RIS, and a user equipment (UE) is defined as the fundamental unit of analysis for ternary stochastic geometry. Furthermore, we extend Campbell’s theorem and propose an approximate probability generating function for ternary stochastic geometry. Utilizing the theoretical framework of ternary stochastic geometry, we derive and analyze performance metrics of a RIS-assisted UDN system, such as coverage probability, area spectral efficiency, area energy efficiency, and energy coverage efficiency. Simulation results show that RIS can significantly enhance system performance, particularly for UEs with high signal-to-interference-plus-noise ratios, exhibiting a phenomenon similar to the Matthew effect.