To address the challenge of accurately identifying tree-related high-impedance earth faults (THIEFs), a method based on long-term fluctuations in zero-sequence current is proposed. The analysis of the recorded data from staged tests in a real test network reveals the development patterns of THIEFs and their zero-sequence characteristics during the fault process. It was found that, over an extended duration, the fluctuation characteristics of the zero-sequence current RMS value curves for THIEFs differ significantly from those of the other types of high-impedance earth faults (HIEFs). By applying approximate arc length parameterization, the RMS value curve is standardized. The curvature standard deviation, the average curvature variation rate, and its standard deviation are used as feature parameters. An identification method based on an improved grey wolf optimization probabilistic neural network is constructed. Validation with staged test results demonstrates that the proposed method achieves a success rate of 97.5%, accurately distinguishing THIEFs from other types of HIEF.