To evaluate the operation status of pole?mounted breakers in an intelligent and efficient manner, the random forest (RF) is em?ployed for feature optimization, and the annealing evolution algorithm (AEA) is applied to optimize the parameters of the re?sidual neural network (ResNet). Firstly, a database is constructed, encompassing 22?dimensional operational features of pole?mounted breakers, with their importance indices calculated using the RF. Through the reverse sequence search method, 11 features are determined as inputs. Subsequently, the AEA is employed to optimize the network parameters of ResNet. Sim?ulation results indicate that the RF effectively eliminates feature redundancy and improves the prediction performance of the model. In comparison to traditional prediction models, the proposed AEA?ResNet method significantly improves the accuracy, especially in the recall and precision of minority samples.