With　the　development　of　artificial　intelligence　(AI)　in　modern　society,　more　and　more　research　findings　related　to　AI　are　being　discovered.　Machine　learning　allows　for　automation　and　mechanical　control　compared　to　traditional　manual　control.　This　article　attempts　to　implement　simulation　and　analysis　of　specific　problems　using　some　specific　modules　in　python　code.　Throughout　the　research　process,　three　different　algorithmic　models,　namely　Random　Forest,　Decision　Tree,　and　SVM,　were　chosen　and　used　to　play　Tic　Tac　Toe.　By　using　mathematical　combinations　of　sequences　that　can　be　played　by　the　system,　different　artificial　neural　networks　are　trained,　and　the　training　results　are　compared　and　analyzed　to　ultimately　find　the　most　suitable　algorithmic　model.　The　choice　of　algorithmic　model　can　contribute　to　a　more　accurate　prediction　of　tic-tac-toe　subsequently.　Through　the　training　and　prediction　of　data　sets,　it　is　considered　that　SVM　in　some　cases　have　the　strongest　performance　among　others.　The　model　may　be　more　applicable　to　the　prediction　and　analysis　of　tic-tac-toe　games.　By　analogy,　the　model　should　be　able　to　help　find　more　excellent　performances　in　tic-tac-toe　games.　©　2022　IEEE.