This paper proposes a power cabin temperature inversion method based on POD-RBF surrogate model and feature point KNN correction, aiming to address the challenges of high computational complexity and poor adaptability in numerical simulation methods for calculating the temperature distribution of power cabin under different working conditions. The proposed method utilizes simulated temperature field data obtained from various operating conditions to construct a temperature inversion model for the power cabin using the proper orthogonal decomposition (POD) and radial basis function (RBF) approach. This surrogate model effectively avoids redundant calculations, enabling rapid approximation of the simulated model. Meanwhile, the K-Nearest Neighbor (KNN) algorithm is employed to introduce the feature temperature points into the inversion model for correcting the temperature inversion error and improving the accuracy and adaptability of the in-version. Taking an actual power cabin as an example, the temperature inversion of the power cabin under the specified working condition is conducted. The results show that the proposed method can achieve real-time temperature inversion of the power cabin under the condition of cable current flow and known temperature of feature temperature points, with the maximum relative error between the inversion temperature and the simulation calculation temperature of 0.96%, meeting the engineering application standard.