In view of the problems existing in the process of insulator operation and maintenance, such as too complicated regulations and too dependent on manual identification of operators, this paper presents an insulator condition evaluation method, which uses historical insulator defect images as training samples and realizes the basis of excellent performance of small sample data processing through migration learning. Based on the training of defect recognition model of deep convolution neural network and the feature extraction ability of convolution neural network, the quantification score of insulator defect can be achieved, and the comprehensive state evaluation of insulator can be realized by considering the operation life and external environment with the help of historical samples and expert experience. An example shows that the recognition accuracy of the proposed transfer learning model can reach more than 90% after training, while the recognition accuracy of the new learning model is only 70% under the same sample conditions, and the evaluation model established in this paper can more sensitively reflect the defect status of insulators in daily operation and maintenance. It shows that the evaluation method established in this paper is quite reliable and can provide experience for the daily maintenance arrangement of operation and maintenance personnel.