Acupoint　potential　signal　is　a　kind　of　bioelectric　signal　collected　on　the　skin　surface,　which　has　the　characteristics　of　weak　signal,　strong　noise　and　strong　randomness.　These　characteristics　make　it　difficult　to　extract　acupoint　features,　which　further　affects　the　accuracy　of　acupoint　classification.　This　paper　proposes　an　acupoint　classification　method　combining　signal　processing　with　intelligent　algorithm.　Firstly,　the　signal　is　decomposed　by　CEEMDAN　(Complete　Ensemble　Empirical　Mode　Decomposition　with　Adaptive　Noise),　and　the　decomposed　IMFs　(Intrinsic　Mode　Functions　are　obtained).　Secondly,　the　filtered　modal　set　is　taken　as　input,　and　the　features　are　extracted　by　using　Convolutional　Neural　Networks　(CNN)　to　get　its　deeper　features.　Finally,　the　obtained　feature　parameters　are　input　into　the　support　vector　machine　(SVM)　optimized　DBO:(Dung　Beetle　Optimizer)　for　classification.　The　experimental　results　show　that　CEEEMDAN-CNN-DBO-SVM　model　can　effectively　identify　the　types　of　acupoints,　with　an　average　accuracy　rate　of　93.01%　at　rest　and　90.6%　at　click　stimulation.　The　effect　is　better　than　CNN,　SVM,　CEEMDAN-CNN,　CEEMDAN-SVM,　CNN-SVM　and　other　five　classification　methods.　©　2023　SPIE.