Because　the　carbon　load　inside　a　diesel　particulate　filters　(DPF)　affects　the　DPF　regeneration,　and　the　carbon　load　recognition　is　significant　for　the　particulate　matter　(PM)　emission　control.　It　is　necessary　to　investigate　an　on-board　DPF　carbon　load　recognition　method　because　the　carbon　load　cannot　be　directly　measured　by　sensors.　Aiming　to　build　a　DPF　carbon　load　prediction　model　adopting　the　deep　learning　method,　this　paper　proposes　a　DPF　carbon　load　identification　model　based　on　different　experimental　parameters　using　a　layered　one　dimension　convolutional　neural　network　(1D-CNN)　method.　To　improve　data　validity,　this　paper　adopts　two　data-processing　methods.　The　data　pre-processing　adopts　data　splicing　method　to　complete　the　construction　of　the　original　sample　set,　and　the　data　after-processing　uses　wavelet　packet　transform　method　to　establish　the　feature　sample　sets.　The　model　adopts　the　optimal　feature　dataset　constructed　by　three　input　parameters,　i.e.,　temperature　difference,　pressure　difference,　and　exhaust　mass　flow,　and　has　both　high　training　accuracy　and　test　accuracy　above　90　%.　The　pressure　difference　is　the　most　important　influencing　input　parameter,　and　the　three-parameter　sample　set　(ΔT　+　ΔP　+　Q)　has　great　recognition　accuracy　and　good　model　stability　with　the　high　training　accuracy　and　test　accuracy　as　well　as　less　iteration.　©　2024　Elsevier　Ltd