There　is　a　risk　of　fire　caused　by　series　arc　failure　in　the　operation　of　photovoltaic　(PV)　system.　Therefore,　it　is　required　to　discuss　a　solution　for　rapid　arc　fault　detection.　To　address　the　series　arc　fault　(SAF)　detection　under　different　working　conditions,　a　method　based　on　squeeze-and-excitation　(SE)-inception　multi-input　convolutional　neural　network　(MICNN)　is　proposed.　Firstly,　normalization　and　Hankel-singular　value　decomposition　algorithm　are　used　to　denoise　the　current,　which　effectively　avoid　the　influence　of　switching　frequency　on　the　subsequent　diagnostic　accuracy.　Subsequently,　the　filtered　time-domain　signal　and　the　frequency-domain　signal　after　Fourier　transform　are　input　into　a　variant　one-dimensional　convolutional　neural　network　(1D-CNN)　model　for　training　and　testing.　The　proposed　model　is　characterized　by　transforming　the　traditional　CNN　into　MICNN,　and　introducing　the　inception　network　with　spatial　scaling　function　and　the　SE　network　structure　with　channel　attention　mechanism.　Extensive　simulations　are　performed　to　evaluate　the　efficacy　with　a　desirable　result　of　97.48%,　which　is　superior　to　traditional　methods　such　as　CNN,　wavelet　decomposition,　and　mathematical　statistics.　The　proposed　method　can　not　only　detect　arc　faults　occurring　in　different　locations,　but　also　resist　the　disturbance　of　dynamic　shading,　maximum　power　point　tracking　(MPPT),　strong　wind,　etc.　In　addition,　this　model　achieved　satisfactory　results　in　three　cases　of　long　line　fault,　single　series　and　array　ageing.