Obtaining　the　mechanical　parameters　of　SiCf/SiC　composites　quickly　and　accurately　is　crucial　for　the　performance　evaluation　and　optimal　design　of　novel　turbine　disc　structures.　A　representative　volume　element　(RVE)　model　of　2D　woven　SiCf/SiC　composites　was　developed　using　CT　scanning　and　machine　learning-driven　image　reconstruction　techniques.　The　stress-strain　curve　was　obtained　by　uniaxial　tensile　test,　and　the　anisotropic　mechanical　parameters　were　obtained　by　inverse　analysis　using　a　non-dominated　sorting　genetic　algorithm　(NSGA-II).　Subsequently,　the　uniaxial　tension　simulation　was　carried　out　based　on　the　RVE　model　and　mechanical　parameters.　The　results　show　that　the　simulation　curve　is　in　good　agreement　with　the　test,　and　the　errors　of　initial　modulus　and　peak　stress　were　3.98%　and　2.75%,　respectively.　Finally,　the　finite　element　models　of　the　turbine　disc　with　two　braiding　schemes　were　established　to　simulate　the　damage　of　the　turbine　disc.　And　the　simulation　results　were　verified　by　a　centrifugal　test.　The　failure　modes　of　the　two　kinds　of　turbine　discs　are　similar　to　the　centrifugal　test　results,　and　the　maximum　rotating　speed　was　close　to　the　test　results.　The　findings　of　this　study　provide　a　novel　solution　for　obtaining　the　anisotropic　mechanical　parameters　of　SiCf/SiC　composites　with　different　woven　schemes.