Objective: To learn the depth of paper using feature extraction, combined with critical areas of heat syndrome and related information, X-ray image of hand to analyze bone age children. Methods: The thesis of the X-ray image data preprocessing left hand, the use of depth of depth neural network learning methods, combined with clinical data skeletal age evaluation model to evaluate the effectiveness of the test model. Results: X-ray image of hand artificial feature extraction, combined SVM classification, automatic assessment of skeletal age. The method of automatic assessment of bone age SVM-based feature primarily artificial, SIFT features extracted image, LBP features, characteristics of GCLM, these features are combined, and then used to train the SVM, have some ability to automatically assess bone age assessment based on SVM. Conclusion: This topic X-ray image based on the hand bones, computer vision, machine learning to extract the relevant methods, pretreatment and segmentation of X-ray images of the hand bones, characterized by automatic assessment of bone age, lack the core image of the sample problem.