Fault　recognition　is　an　important　part　of　seismic　interpretation,　but　the　existing　methods’　accuracy　is　not　high　enough.　Convolutional　neural　networks　have　achieved　high　accuracy　in　handwriting　recognition.　Based　on　the　similarity　between　fault　and　handwriting　shape　features,　this　paper　proposes　a　fault　recognition　method　based　on　the　classical　convolutional　neural　network.　Firstly,　a　neural　network　model　suitable　for　fault　recognition　is　designed　based　on　the　successful　LeNet5　model,　which　has　been　used　for　handwriting　recognition.　The　output　layer　of　the　neural　network　is　designed　with　two　neurons　to　judge　whether　the　seismic　sample　points　belong　to　faults.　Additionally,　the　Softmax　Regression　model　is　used　to　replace　the　original　European　radial　basis　function.　This　modification　allows　the　network　to　output　not　only　whether　the　seismic　sample　points　belong　to　faults,　but　also　the　probability　of　belonging　to　or　not　belonging　to　a　fault.　Then,　a　3D　sample　set　and　test　set　are　established,　using　the　most　accurate　manual　fault　recognition　method,　to　train　the　neural　network.　Finally,　the　proposed　method　is　tested　on　actual　seismic　data,　and　the　experimental　results　confirm　the　effectiveness　and　progressiveness　of　the　proposed　approach.　©　(2024).　All　Rights　Reserved.