Convolutional　neural　network　(CNN)　has　been　extensively　used　in　pattern　recognition　of　partial　discharge　(PD)　in　gas-insulated　switchgear　(GIS)　because　of　its　powerful　feature　extraction　ability.　However,　at　this　stage,　manual　trial　and　error　is　needed　to　construct　the　CNN.　Moreover,　the　model　is　designed　for　specific　datasets,　which　will　cause　domain　bias　when　applied　to　a　new　dataset.　Therefore,　a　novel　differentiable　neural　architecture　search　method　is　proposed　to　automatically　construct　a　GIS　PD　pattern　recognition　model.　First,　a　factorized　hierarchical　search　space　is　used　to　design　the　CNN　architecture.　Then,　a　discrete　search　space　is　relaxed　into　a　continuous　search　space　through　a　search　strategy　based　on　Gumbel–softmax.　Experiments　show　that　the　recognition　accuracy　of　the　proposed　method　can　reach　97.625%.　Furthermore,　the　proposed　method　has　strong　robustness　and　high　precision　against　noise　and　strong　tolerance　for　unbalanced　datasets.　©　2022　Elsevier　Ltd