In　recent　years,　machine　learning　has　made　considerable　progress　in　various　fields.　The　training　of　deep　learning　algorithm　model　requires　a　large　amount　of　data,　and　a　large　amount　of　data　annotation　is　very　costly.　In　this　case,　the　use　of　simulation　data　can　provide　great　convenience.　Using　the　three-dimensional　reconstruction　algorithm　to　construct　a　three-dimensional　model　of　the　real　scene　can　speed　up　the　construction　of　simulation　data.　The　model　has　problems　such　as　model　damage　and　model　fragmentation.　The　repair　model　takes　a　long　time　and　the　repair　effect　is　poor.　To　solve　these　problems,　we　designed　and　developed　a　simulation　training　data　generation　system.　The　system　uses　Blender　as　a　development　platform,　introduces　a　three-dimensional　reconstruction　algorithm　to　initialize　the　model,　transforms　the　model　into　a　point　cloud　for　repair　operation,　and　proposes　a　point　cloud　region　adjustment　method　and　a　point　cloud　fast　repair　method.　Finally,　the　refined　model　is　used　to　generate　a　data　set　with　true　values.　The　experimental　results　show　that　the　system　we　designed　can　improve　the　efficiency　and　accuracy　of　data　generation　and　efficiently　generate　simulation　training　data　for　machine　learning.　©　2023　SPIE.