Highly　transparent　material　classification　can　play　an　important　role　in　the　field　of　computer　vision　to　classify　glass　or　plastics　for　recycling　and　for　home　service　robots　to　recognize　transparent　material.　In　these　areas,　there　is　a　need　to　classify　materials　that　are　more　than　73%　transparent,　but　current　transparent　material　classification　methods　cannot　classify　materials　with　full　transparency　levels.　This　paper　proposes　a　highly　transparent　material　classification　method　based　on　the　refractive　index,　reflectivity,　and　transmissivity　features　from　an　imaging　model　of　a　time-of-flight　(ToF)　camera　as　the　classification　feature.　First,　we　use　the　ToF　camera　to　collect　the　depth　and　light　intensity　of　the　transparent　material,　as　well　as　the　scene　information.　The　acquisition　depth　is　distorted　owing　to　the　material　characteristics　of　transparent　materials.　Second,　we　estimate　the　refractive　index,　reflectance,　and　transmittance　from　the　depth　distortion　and　IR　(infrared　rays)　image.　Finally,　we　choose　a　classifier　that　conforms　to　the　nonlinear　characteristics　of　the　data　to　achieve　transparent　material　classification.　The　method＇s　classification　accuracy　reached　94.1%　in　an　experiment,　indicating　that　our　method　considers　the　unique　phenomenon　of　highly　transparent　materials　reflecting　against　the　background,　incorporates　this　phenomenon　into　the　ToF　distance　model,　it　can　extract　material　features　that　express　the　characteristics　of　highly　transparent　materials,　making　it　applicable　to　the　classification　of　transparent　materials　at　all　levels　of　transparency.