In　the　process　of　intelligent　bone　age　assessment　for　Chinese　ethnicity,　generalized　convolutional　network　models　are　not　well　targeted　in　extracting　specific　features　in　medical　skeletal　images　and　lack　specificity　in　training　and　predicting　skeletal　developmental　features　in　different　ethnicities.　This　study　aims　to　propose　a　hybrid　improved　deep　residual　network　model,　ZH05-DL-ResNet50,　focusing　on　intelligent　bone　age　assessment　for　Chinese　ethnicity.　In　the　process　of　building　the　ZH05-DL-ResNet50　model.　First,　multiple　overlapping　texture　enhancement　layers　are　introduced　through　combinatorial　optimization,　which　can　better　characterize　the　global　features　of　hand　bone　radiographs　while　using　less　texture　information,　reducing　the　interference　of　redundant　information　and　freeing　up　computational　power;　second,　the　China-05　＇　s　spatial　focusing　mechanism　is　designed　so　that　the　model　can　intelligently　focus　on　the　region　of　interest,　efficiently　locate　and　automatically　learn　key　image　information;　Finally,　the　model　can　be　used　for　intelligent　bone　age　assessment　of　Chinese　ethnicity　by　constructing　a　50-layer　residual　depth　network,　the　superposition　enhancement　layer　and　the　focusing　mechanism　are　fused　to　form　the　ZH05-DL-ResNet50　model,　which　mitigates　the　problem　of　gradient　disappearance　and　explosion　caused　by　the　increase　of　network　depth,　and　reduces　the　information　loss　and　depletion　of　the　multi-layer　texture　superposition　features　in　the　focus　region　of　interest.　The　training　was　performed　using　the　Chinese　ethnographic　dataset　provided　and　created　by　Tsinghua　Changgeng　Hospital,　where　10　%　of　the　radiographs　were　used　as　the　test　set　and　the　rest　as　the　training　and　validation　sets.　The　overall　performance　of　the　models　was　estimated　by　comparing　the　average　accuracy　and　loss　values　of　different　models,　and　the　overall　performance　between　model　training　and　model　design　was　evaluated　using　the　data　distribution　(Distribution)　and　histogram　of　bias　weight　distribution　(Histogram).　The　results　showed　that　the　average　accuracy　of　the　bone　age　estimation　model　was　98.1　%　and　the　average　error　on　the　test　set　was　0.312　years.　Evaluation　of　the　model　visualization　and　accuracy　comparison　showed　that　the　model　performed　well　and　was　more　appropriate　and　accurate　than　other　bone　age　estimation　models.　Therefore,　the　first　intelligent　bone　age　assessment　model　for　Chinese　ethnicity,　ZH05-DL-ResNet50,　was　created,　which　differs　from　the　untargeted　nature　of　other　intelligent　bone　age　assessment　models,　and　is　used　to　train　and　predict　hand　bone　radiographs　more　appropriately　and　accurately　for　Chinese　ethnicity.