At　present,　personal　identification　based　on　code-modulated　visual-evoked　potentials　is　increasingly　attracting　people＇s　attentions.　Some　convolutional　neural　networks　(CNN)　have　been　applied　to　recognize　biomarkers　based　on　code-modulated　visual-evoked　potentials　(c-VEP)　for　personal　identification.　However,　the　ordinary　CNNs　encountered　difficulties　in　grasping　the　basic　characteristics　of　c-VEP　to　achieve　a　satisfactory　performance.　In　this　study,　we　proposed　a　lightweight　convolutional　neural　network　(LCNN)　to　recognize　the　c-VEP　biomarkers　in　the　tasks　of　personal　identification.　LCNN　is　composed　of　two　parallel　sub-nets,　which　correspond　respectively　to　two　profiles　of　a　c-VEP　sample　and　both　include　two　blocks.　The　two　blocks　both　contain　a　two-step　convolutional　sequence.　The　LCNN　model　is　fitted　by　minimizing　the　categorical　cross-entropy　loss　function.　The　goal　of　LCNN　is　to　specifically　handle　the　Electroencephalogram　(EEG)　data　in　the　tasks　of　personal　identification　based　on　c-VEP.　We　recruited　20　subjects　to　participate　in　our　personal　identification　experiments　based　on　c-VEP.　In　the　EEG　dataset　of　the　20　subjects,　LCNN　reached　the　recognition　accuracy　of　99%.　The　result　shows　that　the　design　of　LCNN　is　suitable　for　recognizing　the　c-VEP　biomarkers.　©　2021　IEEE.