Powder　metallurgy　is　the　optimal　method　for　the　consolidation　and　preparation　of　W(Mo)　alloys,　which　exhibit　excellent　application　prospects　at　high　temperatures.　The　properties　of　W(Mo)　alloys　are　closely　related　to　the　sintered　density.　However,　controlling　the　sintered　density　and　porosity　of　these　alloys　is　still　challenging.　In　the　past,　the　regulation　methods　mainly　focused　on　time-consuming　and　costly　trial-and-error　experiments.　In　this　study,　the　sintering　data　for　more　than　a　dozen　W(Mo)　alloys　constituted　a　small-scale　dataset,　including　both　solid　and　liquid　phases　sintering.　Furthermore,　simple　descriptors　were　used　to　predict　the　sintered　density　of　W(Mo)　alloys　based　on　the　descriptor　selection　strategy　and　machine　learning　method　(ML),　where　ML　algorithm　included　the　least　absolute　shrinkage　and　selection　operator　(Lasso)　regression,　k-nearest　neighbor　(k-NN),　random　forest　(RF),　and　multi-layer　perceptron　(MLP).　The　results　showed　that　the　interpretable　descriptors　extracted　by　our　proposed　selection　strategy　and　the　MLP　neural　network　achieved　a　high　prediction　accuracy　(R　＞　0.950).　By　further　predicting　the　sintered　density　of　W(Mo)　alloys　using　different　sintering　processes,　the　error　between　the　predicted　and　experimental　values　was　less　than　0.063,　confirming　the　application　potential　of　the　model.