Reliable　identification　of　gunshot　events　is　crucial　for　reducing　gun　violence　and　enhancing　public　safety.　However,　current　gunshot　detection　and　recognition　methods　are　still　affected　by　complex　shooting　scenarios,　various　nongunshot　events,　diverse　firearm　types,　and　scarce　gunshot　datasets.　To　address　these　issues,　based　on　triaxial　acceleration　of　guns,　a　novel　general　deep　transfer　learning　approach　is　proposed　for　gunshot　detection　and　recognition,　which　combines　a　temporal　deep　learning　model　with　transfer　learning　and　automated　machine　learning　(AutoML)　to　improve　the　accuracy,　reliability　and　generalization　performance.　First,　a　new　gunshot　recognition　model　named　as　MobileNetTime　is　proposed　for　the　two-class　gunshot　event　detection,　three-class　coarse　firearm　recognition,　and　15-class　fine　firearm　recognition,　which　utilizes　1-D　convolution　and　inverted　residual　modules　to　autonomously　extract　higher-level　features　from　the　time　series　acceleration　data.　Second,　considering　the　impact　of　nongunshot　events,　the　AutoML　is　employed　for　model　fine　tuning,　to　transfer　the　pretrained　MobileNetTime　from　the　handgun　to　various　firearm　types.　In　addition,　we　propose　a　low-power　versatile　gunshot　recognition　system　framework　employing　a　triaxial　accelerometer　for　both　of　wrist-worn　and　gun-embedded　scenarios,　which　adopts　a　two-stage　wake-up　mechanism　that　selectively　monitors　gunshot　events　using　temporal　and　spectral　energy　features.　The　experimental　results　on　the　two　gunshot　datasets　DGUWA　and　GRD　show　that　the　proposed　model　can　achieve　up　to　100%　accuracy　on　the　DGUWA　dataset　and　98.98%　accuracy　on　the　GRD　dataset　for　the　two-class　gunshot　detection.　Moreover,　the　proposed　deep　transfer　learning　approach　achieves　a　98.98%　accuracy　for　16-class　firearm　classification,　which　is　6.21%　higher　than　the　model　without　transfer　learning.