In view of the current man-made survey of land resource utilization, the task has high labor cost and high labor intensity, and it has high theoretical and practical value for automatic change detection. In this paper, the Deep Belief Networks (DBN) is combined with the high-resolution remote sensing image to detect the change of the building. In order to solve the problem of lack of completeness of the building and noise in the space caused by the misjudgment of the DBN in the change detection, The DBN and mathematical morphology fusion model is used to detect building changes in high-resolution remote sensing images. On the basis of remote sensing image preprocessing, a small number of obvious changes and unchanging samples are marked, and a large number of tagged samples are trained from the marked area to train the fusion model classifier to detect changes in the building. The accuracy of the detection method is 94.76%, the recall rate is 87.63%, and the F1 value is 91.06%. The experimental results show that the method can provide an effective basis for building change detection.