The　brightness　temperature　(TB)　features　extracted　from　Chang’e-2　Lunar　Microwave　Sounder　(CELMS)　data　represent　the　passive　microwave　thermal　emission　(MTE)　from　the　lunar　regolith　at　different　depths.　However,　there　have　been　few　studies　assessing　the　importance　and　contribution　of　each　TB　feature　for　mapping　mare　basalt　units.　In　this　study,　a　unified　framework　of　TB　features　analysis　is　proposed　through　a　case　study　of　Mare　Fecunditatis,　which　is　a　large　basalt　basin　on　the　eastern　nearside　of　the　Moon.　Firstly,　TB　maps　are　generated　from　original　CELMS　data.　Next,　all　TB　features　are　evaluated　systematically　using　a　range　of　analytical　approaches.　The　Pearson　coefficient　is　used　to　compute　the　correlation　of　features　and　basalt　classes.　Two　distance　metrics,　normalized　distance　and　J-S　divergence,　are　selected　to　measure　the　discrimination　of　basalt　units　by　each　TB　feature.　Their　contributions　to　basalt　classification　are　quantitatively　evaluated　by　the　ReliefF　method　and　out-of-bag　(OOB)　importance　index.　Then,　principal　component　analysis　(PCA)　is　applied　to　reduce　the　dimension　of　TB　features　and　analyze　the　feature　space.　Finally,　a　new　geological　map　of　Mare　Fecunditatis　is　generated　using　CELMS　data　based　on　a　random　forest　(RF)　classifier.　The　results　will　be　of　great　significance　in　utilizing　CELMS　data　more　widely　as　an　additional　tool　to　study　the　geological　structure　of　the　lunar　basalt　basin.　©　2023　by　the　authors.