Nowadays,　despite　the　popularity　of　smartphones　in　our　daily　lives,　emails　are　still　the　most　widespread　way　for　people　to　communicate　and　exchange　information　in　business　and　many　other　circumstances.　However,　a　tremendous　problem　called　malicious　email,　also　known　as　spam,　bothers　people　and　demands　constant　detection　and　block.　This　paper　discusses　machine　learning　approaches　to　achieve　malicious　email　detection.　The　data　for　training　is　more　than　10,000　raw　emails　with　Chinese　text　as　well　as　features　including　server　name,　IP　address,　timestamp,　and　content.　First,　the　contents　are　split　into　words　via　feature　engineering.　Then,　the　malicious　email　detection　is　carried　out　by　multiple　machines　learning　methods,　including　Naïve　Bayes,　Decision　Tree,　Random　Forest,　Gradient　Boosting　Decision　Tree　(GBDT),　Support　Vector　Machine　(SVM),　and　K　Nearest　Neighbor　(KNN),　respectively.　The　performance　of　these　models　is　evaluated　by　criteria　like　precision,　recall　score,　F1　score,　and　time　cost.　It　is　shown　that　the　Naïve　Bayes　model　yields　the　best　results,　with　the　F1　score　being　higher　than　97%,　which　indicates　that　our　model　is　promising　in　practice.　©　2021　SPIE.