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In view of the inefficiency of the traditional identification method of disassembled waste electrical equipment, a method of identifying waste electrical equipment with custom features was proposed. Firstly, the image of waste electrical appliances was segmtioned from the background by object segmentation algorithm. Then, the shape features of the waste electrical appliances and the deep features extracted by convolutional neural network were extracted. PCA algorithm was used to optimize the extracted shape features, and the optimized shape features were splice with the deep features. Finally, the spliced feature vectors are trained on the three SVM binary classifiers, and the classification model of waste electrical appliances is obtained. The results show that the recognition accuracy of the splice feature vector is high, up to 91.21%, which can effectively realize the intelligent identification of waste electrical appliances.