This paper proposes a BOF(bag of features image) retrieval algorithm to classify electrical equipment images. First, the location of feature points is determined by speed up robust features (SURF) algorithm, and a high-dimensional feature description operator is constructed to describe and count the features. Then, the K-means clustering algorithm is used to deal with the feature description operators, and the independent visual vocabularies are collected into a specific number of codebooks. The feature description operators in codebooks are quantified and weighted, and the eigenvector histogram is used to represent the entire image. Finally, the high-dimensional feature vectors of the training set images are used for machine learning, and the unknown images are classified quickly and accurately. Electrical equipment images under natural light conditions and infrared images under the working conditions of electrical equipment are taken as two experimental sample sets for classification test. The results show that the algorithm can classify different image sets quickly and accurately with the highest accuracy of 95.59%.