Numerical　simulation　technology　is　widely　used　in　material　forming　process　optimization　and　mold　design.　Although　large　volumes　of　simulation　result　data　can　be　obtained,　it　is　difficult　to　directly　derive　the　relationship　between　the　forming　quality　and　the　forming　process　parameters.　To　extract　the　potential　knowledge　latent　in　the　simulation　results,　a　systematic,　robust,　and　efficient　knowledge　discovery　technology　is　necessary,　such　as　artificial　intelligence　technology,　which　has　become　one　of　the　important　research　directions　of　material　forming　and　processing.　In　this　study　the　deep　drawing　process　of　a　motorcycle　fuel　tank　cover　was　taken　as　an　example.　A　motorcycle　fuel　tank　has　complicated　surfaces　and　local　small　fillets,　and　during　its　formation,　the　side　wall　and　fillet　are　likely　to　wrinkle　and　rupture,　respectively,　because　of　local　deep　and　uneven　deformation.　It　is　important　to　determine　the　forming　parameters　to　produce　high　quality　tank　cover　that　satisfies　the　surface　quality　requirements.　Compared　with　the　iterative　dichotomiser　3　(ID3)　decision　tree　algorithm,　the　classification　and　regression　decision　tree　(CART)　algorithm　is　advantageous　in　terms　of　faster　computation　speed,　higher　stability,　and　supporting　multiple　segmentation　of　continuous　data.　Furthermore,　compared　with　other　algorithms　such　as　support　vector　machines　(SVM)　and　logistic　regression　(LR),　using　the　CART　decision　tree　algorithm,　the　decision　tree　diagram　can　be　established,　and　knowledge　rules　can　be　visually　extracted.　Combining　the　artificial　intelligence　technology　of　CART　decision　tree　and　the　model　cross　validation　method　of　F1　score,　Scikit-Learn,　an　open-source　library　of　Python　platform　was　used　to　carry　out　knowledge　discovery　from　the　numerical　simulation　results　of　the　tank　cover　deep　drawing　process.　The　key　forming　process　parameters　of　the　tank　cover,　which　are　blank　holder　force,　the　height　of　the　draw　bead,　and　radius　of　the　die　fillet,　were　identified.　The　optimal　eigenvalues　and　the　optimal　segmentation　points　of　CART　decision　tree　were　selected　according　to　the　minimization　criteria　of　Gini　index,　and　the　process　rules　were　extracted　from　the　CART　decision　tree　of　the　forming　quality　index　and　the　established　process　parameters.　The　tank　cover　drawing　process　example　shows　that　the　knowledge　discovery　technology　based　on　CART　decision　tree　theory　is　a　feasible　way　to　mine　potential　knowledge　from　the　numerical　simulation　results　of　material　forming　process.　©　All　right　reserved.