Aiming　at　the　insufficiency　of　the　generalization　ability　of　open-loop　models　and　the　drawbacks　of　deep　neural　network　structure　for　the　existing　detection　methods　of　the　insulator　self-exploding　state,　drawn　on　the　experience　of　migration　learning　and　closed-loop　control,　this　paper　explores　an　intelligent　cognition　method　of　self-exploding　state　of　glass　insulator　based　on　deep　migration　learning,　to　imitate　human　cognition　model.　Firstly,　for　the　pretreated　glass　insulator　images,　the　interlaced　group　convolution　strategy　was　employed　to　reconstruct　the　convolution　layer　of　GoogLeNet　network,　which　reduced　the　complexity　of　network　convolution.　Secondly,　based　on　the　adaptive　convolution　module　group,　the　data　structure　of　dynamic　feature　space　of　the　insulator　images　was　built　with　certain　mapping　relationship　from　global　to　local,　and　then　the　discriminative　measure　index　was　used　to　evaluate　the　difference　cognition　information　of　the　feature　space　to　enhance　the　interpretability　of　the　compact　feature　space.　Thirdly,　the　compact　fully　connected　feature　vector　was　sent　to　stochastic　configuration　networks　(SCNs)　with　universal　approximation　ability　to　establish　the　insulator　image　classification　criterion　with　strong　generalization　ability.　Finally,　imitating　human　thinking　mode,　based　on　the　generalized　error　and　entropy　theory,　the　evaluation　index　of　the　objective　optimization　function　with　entropy　form　for　the　uncertain　cognition　results　of　glass　insulator　images　was　built,　to　real-time　evaluate　the　cognition　results　of　insulator　self-　exploding　state.　The　dynamic　migration　learning　mechanism　is　constructed　to　realize　the　self-optimization　adjustment　and　reconstruction　of　the　multi-level　differentiated　feature　space　of　glass　insulator　self-explosion　state　and　its　classification　criteria.　The　experimental　results　show　the　feasibility　and　effectiveness　of　the　proposed　method.　©　2020　Chin.　Soc.　for　Elec.　Eng.