Recently,　with　the　development　of　both　3D　sensors　and　3D　virtual　network　that　bring　the　needs　of　interaction　with　the　real　world,　many　3D　applications　burst　out.　However,　it　is　difficult　to　understanding　these　three-dimensional　scenes　with　a　fixed　program.　Then,　a　data-driven　method　is　required　to　process　these　3D　data,　which　brings　a　strong　demand　of　3D　Deep　Learning　in　3D　data.　Towards　this　goal,　with　an　end-to-end　deep　learning,　the　experiment　is　based　on　PointNet++,　a　well　proposed　method　for　feature　extraction.　The　experiment　optimizes　the　network　structure　and　parameters　to　improve　the　classification　results.　Finally,　the　network　is　applied　to　tooth　model　for　classification　and　identification　so　that　the　dental　model　can　be　found　from　different　perspectives.　©　2019　IEEE.