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Most existing methods aiming to solve the fault identification problem of metal oxide arresters (MOAs) are limited by strong subjectivity in judgment, the significant impact of environmental temperature and humidity on the online monitoring of the resistance current, and poor generalization ability. Therefore, in this article, we propose an MOA fault identification method that combines suppressing environmental temperature and humidity interference with a stacked autoencoder (SAE). Firstly, a functional relationship model between resistance current and environmental temperature and humidity is established. Then, a temperature and humidity interference suppression method based on weighted nonlinear surface modeling is proposed to normalize the resistance current to the same reference temperature and humidity conditions. Finally, an MOA fault identification method combining the suppression of environmental temperature and humidity interference with an SAE is proposed. Furthermore, a comprehensive comparison is conducted on the recall, accuracy, F1-score, and average accuracy of support vector machine, random forest, logistic regression, and SAE classification algorithms in three different scenarios to demonstrate the effectiveness of the proposed method. The results indicate that environmental temperature and humidity interference suppression for resistive current prior to MOA fault classification significantly reduce the number of false alarms. Compared with other methods, the MOA fault identification method, which combines environmental temperature and humidity interference suppression with an SAE, has the highest average accuracy of 99.7%. [ABSTRACT FROM AUTHOR]