The　mechanical　properties　of　high　entropy　alloys　are　highly　dependent　on　phase　structure.　Therefore,　accurate　prediction　of　the　phase　structure　is　a　guide　to　composition　selection　and　performance　enhancement,　and　it　saves　both　experimental　and　financial　expenditure.　In　this　study,　seven　classification　models　were　selected　to　predict　the　phase　structure　of　high-entropy　alloys　and　six　regression　models　were　selected　to　predict　the　hardness,　and　it　was　found　that　the　XGB　model　performed　best　in　both　classification　and　regression　predictions,　where　in　the　prediction　phase　structure,　the　XGB　model　has　an　error　rate　of　4.5%　in　the　10%　prediction　set,　11.6%　in　the　20%　prediction　set　and　6.25%　in　the　30%　prediction　set.　In　predicting　the　hardness,　the　RMSE　and　MAE　evaluation　indexes　of　the　XGB　model　are　52.66,　26.49.　The　δ　(atomic　radius　difference)　feature　parameter　having　the　strongest　contribution　to　the　phase　formation　in　the　high-entropy　alloys.　In　addition,　valence　electron　concentration(VEC),　y/n　BCC　(The　presence　or　absence　of　BCC)　and　y/n　lm　(The　presence　or　absence　of　other　phases)　were　key　features　affecting　the　hardness.　Predictive　analysis　of　two　high-entropy　alloys,　AlCoCrFeNi　and　AlCoCrFeNiTi0.5,　and　a　characterization　effect　map　based　on　the　Shapley　additive　explanations　(SHAP)　framework　were　developed　to　provide　decision　support　for　hardness　assessment.　©　2023　Elsevier　Ltd