30 default bonds and 468 non-default samples were selected as research samples from Shanghai Stock Exchange and Shenzhen Stock Exchange, whether the bonds were defaulted was set as a binary classification problem for identification and analysis, and ADmR-AdaboostSVM classification model based on SVM was constructed for this problem. This article selects 16 early warning indicators from such four evaluation factors as enterprise capital structure, profitability, cash flow, and solvency, uses ADASYN method for oversampling and synthesizing new sample points, introduces mRMR method of feature extraction to bonds default field to obtain such four variables as long-term debt ratio, the rate of return on capital, profit margin on costs, and equity ratio as final early warning indicators of bonds default, and on this basis, uses AdaboostSVM model to conduct risk identification. Research results show that the sample unbalance problem is overcome during modeling process to make classification accuracy significantly improved, at the same time, the accuracy for identifying defaulted bonds is further achieved by solving the problem of high-dimensional data redundancy. Repeated verifications show that this model has strong robustness and effectiveness and has certain application value.