The　rock　mass　class　identification　of　the　tunnel　face　is　a　key　problem　for　TBM　operating　parameters　optimization　and　subsequent　tunnel　support　measures　selection.　This　study　presents　a　rock　mass　class　identification　method　by　monitoring　and　classifying　TBM　cutterhead　vibration　signals.　Firstly,　vibration　signals　were　collected　by　a　set　of　cutterhead　vibration　monitoring　system　installed　on　the　TBM　cutterhead　during　TBM　tunnelling.　The　corresponding　rock　mass　classification　were　conducted　along　the　excavated　tunnel　field　investigation.　Secondly,　time　statistics　and　waveform,　power　spectrum　frequency,　nonlinear　and　time-frequency　domain　were　extracted　from　the　TBM　cutterhead　vibration　signal.　18　features　were　selected　by　Boruta-SHAP　feature　selection　method　as　important　feature　set.　Based　on　the　result　analysis　of　different　machine　learning　models,　the　XGBoost　model　was　the　best　model　used　to　identify　the　rock　mass　class.　Its　accuracy　was　up　to　98.79　%　on　the　test　set.　Finally,　the　feature　sensitivity　analysis　by　SHAP　interpretation　showed　that　energy　entropy,　Imf6e　and　kurtosis　were　the　most　sensitive　features　for　different　rock　mass　classes.