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%.