The　hardness　of　cemented　carbides　is　a　fundamental　property　that　plays　a　significant　role　in　their　design,　preparation,　and　application　evaluation.　This　study　aims　to　identify　the　critical　factors　affecting　the　hardness　of　WC-Co　cemented　carbides　and　develop　a　high-throughput　predictive　model　for　hardness.　A　dataset　consisting　of　raw　material　composition,　sintering　parameters　and　characterization　results　of　cemented　carbides　is　constructed　in　which　the　hardness　of　cemented　carbide　is　set　as　the　target　variable.　By　analyzing　the　Pearson　correlation　coefficient,　Shapley　additive　explanations　(SHAP)　results,　WC　grain　size　and　Co　content　are　determined　to　be　the　key　characteristics　influencing　the　hardness　of　cemented　carbide.　Subsequently,　machine　learning　models　such　as　support　vector　regression　(SVR),　polynomial　regression　(PR),　gradient　boosting　decision　tree　(GBDT),　and　random　forest　(RF)　are　optimized　to　construct　prediction　models　for　hardness.　Evaluations　using　10-fold　cross-validation　demonstrate　that　the　GBDT　algorithm　model　exhibits　the　highest　accuracy　and　strong　generalization　capability,　making　it　most　suitable　for　predicting　and　analyzing　the　hardness　of　cemented　carbides.　Based　on　predictions　from　GBDT　algorithm　model,　PR　algorithm　model　is　established　to　achieve　high-precision　interpretable　prediction　of　the　hardness　of　cemented　carbides.　As　a　result,　a　quantitative　relationship　between　hardness　and　Co　content　and　WC　grain　size　is　obtained,　demonstrating　that　reducing　grain　size　and　Co　content　is　the　key　to　obtaining　high　hardness　of　cemented　carbide.　This　research　provides　a　data-driven　method　for　accurately　and　efficiently　predicting　cemented　carbide　properties,　presenting　valuable　insights　for　the　design　and　development　of　high-performance　cemented　carbide　materials.　[GRAPHICS]　.