The invention relates to a relevance feedback method based on a transfinite learning machine. The relevance feedback method comprises the following steps: inputting an inquiry image; retrieving the image to obtain a retrieval result, and marking a result by a user; respectively extracting an SIFT characteristic, a Colour characteristic and an LBP characteristic from the marked image; training three basis classifiers by utilizing the three kinds of characteristics; respectively putting the image in a retrieval image library in the three basis classifiers, voting according to a prediction result, and automatically marking each unmarked image; training and updating the classifiers again; classifying the image in the image library; and returning a result. The relevance feedback method disclosed by the invention is established on the basis of the transfinite learning machine; human-computer interaction is carried out by introducing the inquiry intention of human beings; learning data are enriched by effectively utilizing the unmarked image in the image library; therefore, the image feedback precision is greatly increased; furthermore, the processing speed is controlled well; expression of the image in a computer accords with understanding of human beings to image semantics well; and thus, the relevance feedback method has a good feedback effect.