Objective:　Deep　learning　and　neural　network　models　are　new　research　directions　in　the　field　of　machine　learning　and　artificial　intelligence.　Deep　learning　has　made　breakthroughs　in　image　recognition　and　speech　recognition　applications,　and　has　also　shown　unique　advantages　in　face　recognition　and　information　retrieval,　and　has　been　widely　used.　Methods:　Thin-layer　computed　tomography　(CT)　scan　and　multiplanar　reconstruction　(MPR)　and　volume　reconstruction　(VR)　techniques　were　used　to　perform　CT　thin-slice　scan　and　volume　of　the　bilateral　clavicle　sternum　at　548　number　of　15　similar　to　25　years　old.　Reproduction　(volume　rendering,　VR)　and　three-dimensional　image　recombination,　measuring　and　calculating　the　longest　diameter　of　the　sternal　end　of　the　bilateral　clavicle,　the　longest　diameter　of　the　metaphysis　and　its　length　ratio,　the　area　of　the　epiphysis,　the　area　of　the　metaphysis　and　its　area　ratio,　etc.　We　establish　a　mathematical　model　of　bone　age　inference,　and　then　substitute　50　training　samples　into　the　mathematical　model　to　test　the　accuracy　of　the　model.　Results:　There　was　a　statistically　significant　difference　between　the　male　and　female　sex　ratios　in　the　same　age　group　(P　＜　0.05).　The　established　mathematical　model　shows　that　the　developmental　law　of　the　sternal　skeletal　bone　is　highly　correlated　with　the　biological　age.　The　accuracy　of　all　models　is　70.5%　(　+/-　1.0　years　old)　and　82.5%　(　+/-　1.5　years).　Skeletal　X-ray　images　show　different　gradation　changes　in　black　and　white,　with　black-and-white　contrast　and　hierarchical　image　features.　Based　on　the　advantages　of　deep　learning　in　image　recognition,　we　combine　it　with　bone　age　assessment　research　to　build　a　forensic　bone　age　automation.　Conclusions:　This　paper　harnesses　the　basic　concepts　of　deep　learning　and　its　network　structure,　and　expounds　the　research　progress　of　deep　learning　in　image　recognition　in　different　research　fields　at　home　and　abroad　in　recent　years.　as　well　as　the　advantages　and　application　prospects　of　deep　learning　in　bone　age　assessment.