The perovskite prototype is one of the most promising solar cell materials. However, perovskite suffers from a phase transition leading to thermodynamic instability, which tightly influences the solar cell operation performance. Thus, modulating transition dynamics would extend its lifetime, which needs an in-depth understanding of the potential energy surface (PES) and the phase transition kinetics at the atomic level. In this work, taking CsPbI3 as an example of a perovskite prototype, we map out the PES and resolve the three lowest energy barrier paths of γ-CsPbI3 degradation by using a stochastic surface walking method integrated with high-dimensional neural-network potential. Path I is γ-CsPbI3 to hexagonal δ′-CsPbI3, a five-step transition with (110)Pv to (001)hex with the energy barrier 0.25 eV/f.u.; Path II is γ-CsPbI3 to cmcm-CsPbI3, a two-step transition with an over all energy barrier 0.22 eV/f.u. and (001)Pv//(110)cmcm + [010]Pv//[001]cmcm; Path III is γ-CsPbI3 to δ-CsPbI3, a one-step transition without forming an inherent interface, with the highest energy barrier 0.34 eV/f.u. Interestingly, We find that with the substitution of the A-site and/or B-site by other atoms, such as Bi and Te, the γ-CsPbI3 to δ-CsPbI3 transition could be extensively hindered. In this work, by resolving the potential energy surface, we not only reveal the degradation mechanism at the atomic level but also find a way to design perovskites with high and long-term stability.