The　use　of　seismic　waves　to　explore　the　subsurface　underlying　the　ground　is　a　widely　used　method　in　the　oil　industry,　since　different　kinds　of　the　rocks　and　mediums　have　different　reflection　rate　of　the　seismic　waves,　so　the　amplitude　of　the　reflected　waves　can　unraveling　the　geological　structure　and　lithologic　character　of　a　certain　area　under　the　ground,　but　the　management　and　processing　of　seismic　wave　data　often　affects　the　efficiency　of　oil　exploration　and　development.　Different　kinds　of　the　seismic　data　bulk　are　always　mixed　and　hard　to　be　classified　manually.　This　paper　presents　a　classification　model　for　four　main　types　of　seismic　data,　and　proposed　a　classification　method　based　on　Mel-spectrum.　An　accuracy　of　98.32%　was　achieved　using　pre-trained　ResNet34　with　transfer　learning　method.　The　accuracy　is　further　improved　compared　with　the　pure　fourier　transformation　method　widely　used　in　previous　studies.　Meanwhile,　the　transfer　learning　method　and　fine-tune　strategy　to　train　the　neural　network　by　training　the　first　N-1　layers　of　the　network　separately　and　then　train　the　fully　connected　layers　further　improves　the　training　efficiency.　Our　model　can　also　be　seen　as　an　efficient　data　quality　control　scheme　for　oil　exploration　and　development.　Meanwhile,　our　method　is　future-proofed,　for　further　improvement　of　the　seismic　data　processing　quality　control　system,　according　to　the　spectrum　characteristics,　this　model　can　be　further　extended　into　a　error　data　classification　model,　reduces　the　workload　of　the　bulk　data　management.　©　2022　IEEE.