Deep　neural　networks　are　important　models　in　the　task　of　recognition　of　waste　electrical　appliances.　However,　deep　neural　networks　are　nonlinear　models,　leading　to　the　lack　of　self-　interpretation　of　appliance　recognition　models　as　a　critical　issue.　We　propose　an　adaptive　sparse　semantic　representation　(ASSR)　model　to　improve　the　self-interpretation　capability　of　appliance　recognition　models.　First,　an　adaptive　sparse　representation　component　is　constructed　to　enhance　the　ability　of　the　appliance　recognition　model　for　sparse　feature　extraction.　Second,　residual　sparse　blocks　are　designed　to　ensure　reliable　network　architecture　and　enhance　the　semantic　representation　ability　of　the　appliance　recognition　model.　Finally,　the　appliance　recognition　model　obtains　an　interpretable　network　architecture　by　computing　the　optimal　sparse　solution,　ensuring　the　strong　self-interpretability　of　the　recognition　model.　The　experimental　results　show　that　the　recognition　accuracy　of　ASSR　is　improved　compared　with　the　traditional　deep　neural　network,　and　ASSR　has　linear　self-　interpretation　capability.　　©　2024　Asian　Control　Association.