Introduction　Currently,　the　main　method　of　identifying　biased　body　types　is　to　use　self-assessment　and　scoring　scales　for　classification,　which　is　not　only　influenced　by　the　subjective　perception　of　the　self-assessor　but　also　takes　a　lot　of　time.　Based　on　this,　this　paper　proposes　to　classify　biased　body　types　based　on　acupuncture　point　signals.　Research　methods　In　this　study,　1562　subjects　were　recruited　to　conduct　the　questionnaire　named　＂　Traditional　Chinese　Medicine　(TCM)　Constitution　and　Classification　Self-Test　Table＂　of　professor　Wang　Qi　with　the　method　of　cross-sectional　survey　and　the　scale　score　was　considered　as　the　standard　to　determine　the　type　of　constitution.　In　addition,　signals　of　24　original　points　on　12　meridians　and　basic　information　including　height,　weight,　age　and　gender　were　collected　and　constructed　as　database.　Then,　a　random　forest-based　feature　selection　method　was　used　to　select　relevant　features　from　the　original　dataset　to　form　the　optimal　feature　subset.　To　address　the　problem　of　unbalanced　dataset　categories,　the　SMOTE+ENN　algorithm　is　used　for　balancing,　and　the　dataset　after　feature　selection　and　balancing　is　trained　to　obtain　a　biased　eight-level　classification　model.　Results　The　biased　body　eight　classification　model　was　trained　100　times　after,　the　mean　of　accuracy,　mean　of　sensitivity,　mean　of　specificity,　and　mean　of　F1　score　were　75.6%,　75.03%,　75.20%　and　74.79%　respectively.　Conclusion　A　classification　model　based　on　random　forest　feature　selection　and　the　SMOTE+ENN　algorithm　is　instructive　for　the　classification　of　biased　Constitution.　©　VDE　VERLAG　GMBH　-　Berlin　-　Offenbach.