TBM　tunneling　is　the　result　of　interaction　between　the　rock　mass　and　the　machine.　Rapid　identification　of　the　rock　mass　condition　at　the　tunnel　face　is　crucial　for　the　safety　and　efficiency　of　TBM　tunneling.　This　study　was　based　on　the　spiral　ramp　project　of　the　Beishan　Underground　Research　Laboratory.　A　TBM　muck　analysis　system　was　installed　on　the　TBM　conveyor　belt　to　obtain　the　muck　characteristic　and　TBM　tunneling　parameters.　Combining　the　muck　characteristic　parameters,　TBM　tunneling　parameters　and　the　corresponding　rock　mass　classes　at　the　tunnel　face,　a　multi-source　database　was　established.　Subsequently,　machine　learning　models　for　rock　mass　class　identification　were　developed　based　on　TBM　tunneling　parameters,　muck　characteristic　parameters,　and　their　fusion,　respectively.　The　LightGBM　model　based　on　these　fusion　parameters　including　tunneling　and　muck　characteristic　parameters,　significantly　outperforms　other　models,　achieving　an　Accuracy　of　0.934,　an　F1-score　of　0.932,　and　a　Kappa　coefficient　of　0.904.　The　model　was　validated　in　the　subsequent　TBM　tunneling　in　the　same　project.　It　demonstrated　the　reliability　of　the　model　in　practical　applications.　©　2025　Elsevier　Ltd