In response to the demand for accurate assessment of infrared thermal fault characteristics of power equipment,a multi feature aggregated characterization of circuit breaker thermal fault diagnosis rating method is proposed,and the data test is carried out using infrared images of high voltage circuit breakers as examples. Firstly,on the basis of the background separation of high voltage circuit breaker infrared images,the equipment is accurately divided into regions to extract the temperature information of each region. Secondly,the Mean shift and the improved region growth method are applied to fuse and accurately extract the area of the fault heat emitting region. Then,a multi dimensional aggregated characterization matrix is designed to combine the heat emitting area,hot spot temperature,hot spot temperature difference,heat emitting location,temperature rise of two identical positions of the same equipment and other eigenvalues into a multi feature vector matrix,and the on site case data is adopted to construct a correlation library of this vector matrix and HV circuit breaker fault types,levels and treatment opinions. Finally,1002 sets of multi feature vectors from 350 infrared images of high voltage circuit breakers are trained and tested. The results show that the F measure and Kappa coefficients of the multi feature vector data extracted by this method using GWO SVM classifier test are 96% and 95.43%,respectively,which can achieve the all types of diagnostic rating and accurate localization of thermal faults in high voltage circuit breaker equipment.