When high-impedance faults (HIFs) occur in resonant grounded distribution networks, the current that flows is extremely weak, and the noise interference caused by the distribution network operation and the sampling error of the measurement devices further masks the fault characteristics. Consequently, locating a fault section with high sensitivity is difficult. Unlike existing technologies, this study presents a novel fault feature identification framework that addresses this issue. The framework includes three key steps ①utilizing the variable mode decomposition (VMD) method to denoise the fault transient zero-sequence current (TZSC); ② employing a manifold learning algorithm based on t-distributed stochastic neighbor embedding (t-SNE) to further reduce the redundant information of the TZSC after denoising and to visualize fault information in high-dimensional 2D space; and ③ classifying the signal of each measurement point based on the fuzzy clustering method and combining the network topology structure to determine the fault section location. Numerical simulations and field testing confirm that the proposed method accurately detects the fault location, even under the influence of strong noise interference.