The extracted features of medical hand bone images by the general deep learning algorithm can’t well distinguish the differences from images with similar age. It leads to the low prediction accuracy of bone age classifier. An improved bone age classifier, named RIL-MobileNetV3 Large, in accordance with the deep learning-based lightweight neural network MobileNet is designed. A dataset of hand bone is generated by the improved LBP processing layer with fine textures and an attention mechanism for automatic positioning is introduced. It complete the recognition and classification of bone age by learning deep area features in the X-ray of hand bone treated by the processing layer. A lot of training is carried out for tuning accompanied by the experiment on public datasets. The results show that the improved classifier has got a high accuracy of 94.204% and a mean error of 0.350 years in the bone age prediction. The improved lightweight network lays a foundation for mobile, intelligent and portable prediction devices of bone age.