Abstract: In the power system, the electrical contact material plays an indispensable role in the switchgear. When the surface is defective, the resistance of the contact surface will increase, which will cause the contact surface to heat up. In severe cases, the switchgear may fail and affect the quality and service life of high-voltage electrical appliances, and many companies are still using manual inspection for surface quality inspection of parts. Aiming at the above problems, a method for detection and classification of electrical contact surface defects based on machine vision and machine learning was proposed. This method builded a detection platform based on the defect types and characteristics of the electrical contacts, preprocessed the collected images of the electrical contact parts, and used the template matching method to locate the electrical contact parts in the image and judge the quality of the surface, the pre-screened electrical contact image was denoised on the surface, and the Otsu threshold segmentation algorithm was used to segment the defect area. In order to effectively classify the defects, multiple features were extracted for the defect area, and a feature selection algorithm was designed to select the features optimally. The decision tree classifier was used to classify the defects, and the classification accuracy can reach 92.6%. Compared with the Support Vector Machine (SVM) algorithm, the decision tree model is better than the SVM model in terms of classification time and efficiency. The experimental results show that the feature dimension reduction of the classification data set can improve the classification accuracy.