To reveal the law of features change of metal surface topography during fatigue damage, we collected and transformed the three-dimensional surface topography information of Q235 carbon steel specimens into grayscale images at each fatigue damage stage. Then, these images were decomposed and reconstructed using fast discrete shear wave transform to obtain subimages containing roughness, waviness, and shape error. Furthermore, the gray-level co-occurrence matrix was used to describe the characteristics of the subimages' roughness. The variation rules of four characteristic parameters, namely, energy, correlation, contrast, and homogeneity, were obtained for the fatigue damage process. In addition, a series of decomposition layers were taken for multiresolution analysis, and the effects of different decomposition layers on the values of the above-mentioned characteristic parameters were compared and analyzed. Results show that the increasing fatigue cycles decrease the energy and homogeneity values and increase contrast values. The above characteristic values are related to the selection of extraction directions. Before fatigue fracture, the energy and homogeneity values suddenly increase, whereas the contrast value sharply decreases. Thus, a support vector machine classification model was constructed based on the three features, including contrast, energy, and homogeneity, and was used for fatigue damage state assessment of components.