This　article　presents　a　contact　fault　diagnosis　method　of　gas-insulated　switchgear　(GIS)　plug-in　connector　via　magnetic　field　measurement,　magnetic　field　visualization,　and　deep　neural　network　(DNN)　classifiers.　First,　the　surroundingmagnetic　field　of　GIS　plug-in　connector　with　normal　contact　(NC)　condition　and　with　artificially　designed　contact　failures　was　measured　by　the　Hall　sensor　array.　Then,　the　measured　magnetic　field　was　gathered　with　an　original　matrix　of　16　x　16　dimensions.　The　|original　matrix　was　then　visualized　by　the　max-min　normalization　and　correlation　matrix.　Database　containing　11　000　magnetic　field　images　was　labeled　and　segmented　as　training,　validation,　and　test　datasets.　Furthermore,　high-dimensional　features　of　input　magnetic　field　images　were　extracted　by　different　DNN　filters,　including　convolutional　neural　network　(CNN),　simple　recurrent　neural　network　(Sim-RNN),　and　long　short-term　memory　(LSTM)　network.　Then,　extracted　high-dimensional　features　were　fed　into　a　fully　connected　(Fc)　neural　network　with　SoftMax　classifiers　to　identify　different　contact　faults.　Finally,　the　performance　of　different　DNN-based　classifiers　is　compared　by　the　fault　classification　merits,　t-distributed　stochastic　neighbor　embedding　(t-SNE)　feature　clustering,　and　confusion　matrixes.　Results　show　that　the　DNN-based　model　could　achieve　contact　fault　classification　task　with　an　accuracy　of　97.7%　and　F-1_score　of　0.985.　Therefore,　the　proposed　method　is　useful　for　designing　a　high-performance　contact　status　monitoring　system　of　GIS　equipment,　thus　improving　its　operation　safety.