Predicting the performance degradation of epoxy resin coatings under complex multi-factor coupling conditions in substations is critical for the reliability assessment and lifespan prediction of power equipment. To achieve accurate prediction of the degradation rate, the failure mechanisms in high-humidity environments were investigated through systematic analysis of the failure morphology and chemical structure evolution of coating samples after different service periods. By coupling key operational parameters including maximum temperature, daily temperature fluctuation amplitude, vibration acceleration, relative humidity, ultraviolet radiation intensity, and coating service time, a protective performance degradation prediction model was developed using XGBoost algorithm with hyperparameters optimized by Tree-structured Parzen Estimator (TPE). The results demonstrate that the TPE algorithm exhibits superior global search capability and efficiency. The TPE-optimized XGBoost model achieved excellent prediction accuracy with a coefficient of determination (R2) of 0.9574 Further comparative experiments revealed that the TPE-XGBoost model significantly outperforms baseline models including Support Vector Regression and Random Forest, while showing comparable accuracy to the Long Short-Term Memory (LSTM) network but with markedly higher computational efficiency. The model also demonstrates excellent stability and generalization capability across multiple time-period validations.