With　the　increasing　installed　capacity　of　photovoltaic　(PV)　power　generation,　it　has　become　a　significant　challenge　to　detect　abnormalities　and　faults　of　PV　modules　in　a　timely　manner.　Considering　that　all　the　fault　information　of　the　PV　module　is　contained　in　the　current-voltage　(I-V)　curve,　this　pioneering　study　takes　the　I-V　curve　as　the　input　and　proposes　a　PV-fault　identification　method　based　on　improved　deep　residual　shrinkage　networks　(DRSN).　This　method　can　not　only　identify　single　faults　(e.g.,　short-circuit,　partial-shading,　and　abnormal　aging),　but　also　effectively　identify　the　simultaneous　existence　of　hybrid　faults.　Moreover,　it　can　achieve　end-to-end　fault　diagnosis.　The　diagnostic　accuracy　of　the　proposed　method　on　the　measured　data　reaches　97.73%,　is　better　than　the　convolutional　neural　network　(CNN),　the　support　vector　machine　(SVM),　the　deep　residual　network　(ResNet),　and　the　stage-wise　additive　modeling　using　multi-class　exponential　loss　function　based　on　the　classification　and　regression　tree　(SAMME-CART).　In　addition,　the　possibility　of　the　aforementioned　method　running　on　the　Raspberry　Pi　has　been　verified　in　this　study,　which　is　of　great　significance　for　realizing　the　edge　diagnosis　of　PV　fault.