Abstract: Based on the difference and complementarity of three different single feature sets for various types of defect recognition, this paper proposes an adaptive boost classification model based on the ensemble method. First, the eight types of partial discharge physical models are designed. Then, through the partial discharge detection and test system based on ultra-high frequency method, the partial discharge pulse sequence data sets for each defect in a stable discharge under different test voltages are obtained, which are used to verify the proposed method. After that, three kinds of single feature sets are extracted from the data sets. The combined feature set is made by these single feature sets with pairs and three. The final feature set is selected from these feature sets as the input of the classification model through comparative analysis. Finally, using the boosting algorithm and taking the support vector machine as the base classifier to obtain an adaptive boost classification model based on the multi-feature combination method. By using the "unpaired" diversity index based on information entropy to measure the inconsistency between one base classifier and other base classifiers, a series of diverse SVM base classifiers with moderate accuracy rates are obtained. For each defect, 25 samples were obtained at the same test voltage level, and a total of 150 samples were obtained at 6 voltage levels through multiple experiments. The proposed method was compared with the traditional methods using these data sets. The results revealed that the proposed method successfully identified the types of partial discharge insulation defects.