Determining　the　types　and　sizes　of　surface　defects　is　crucial　for　an　evaluation　of　the　surface　quality　of　precision　optical　components.　We　propose　a　cascaded　inversion　algorithm　based　on　a　decision　tree　model　to　address　the　limitations　of　traditional　inversion　algorithms　in　terms　of　inversion　dimension　and　scale　when　angle-resolved　scattering　signals　are　used　to　invert　the　structural　characteristic　parameters　of　surface　defects.　To　construct　the　dataset　needed　to　train　the　model,　an　electromagnetic　simulation　of　the　angle-resolved　scattering　system　was　established　using　the　finite　difference　time　domain　method,　and　the　dataset　was　obtained　through　simulation　calculations.　The　inversion　results　for　the　test　set　data　show　that　the　proposed　algorithm　is　able　to　predict　the　defect　type　and　depth　with　a　precision　having　an　area　under　the　curve　of　0.　99　and　an　average　R(2　)of　0.932,　expanding　the　inversion　dimension.　The　algorithm　also　accurately　predicts　the　width　of　defects　at　different　defect　depths　with　an　average　R-2　of　0.　997,　increasing　the　scale　of　inversion.　The　proposed　algorithm　offers　a　new　approach　to　the　precise　quantitative　analysis　of　small　defects　on　the　surface　of　optical　components.