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The rapid and accurate extraction on cropland information by using remote sensing technology is a key aspect of cropland protection. Taking Shanghe County of Shandong Province as an example, a cropland information extraction method of Landsat 8 OLI images based on multi-seasonal fractal features was proposed. Firstly, the upper fractal signal and lower fractal signal of each pixel of multi-seasonal remote sensing images were calculated by using a blanket covering method, and the fractal characteristics of cropland and other land use types were compared and analyzed. The third scale of the upper fractal signal was selected as the feature scale to extract the spatial distribution of cropland in Shanghe County. Secondly, the land use vector data, Esri land cover data and statistics at the same period were used to evaluate the extraction accuracy of cropland information. Finally, comparative experiments between multi-seasonal fractal extraction with the single season fractal extraction and the existing land use data products were set up to evaluate the accuracies based on the point matching degree and area matching degree, respectively. The results showed that the multi-seasonal data can better reflect the complexity of crop growth and improve the extraction accuracy of cropland information. Different land use types had different signal values at different fractal scales, and their fractal features can clearly depict the differentiations among them at different scales. The evaluated point matching degree and area matching degree of cropland extraction results by using multi-seasonal fractal features based on the land use vector data and Esri land cover data were 87.13% and 89.83%, 99.73% and 97.91%, respectively, which were higher than that of single season fractal extraction. Considering the point matching degree, area matching degree and spatial distribution characteristics, the research method could effectively distinguish cropland and other land use types, which had much better extraction results and a higher consistency with the statistical data. The method developed can accurately extract the cropland information and provide technical supports for the dynamic monitoring and damage assessment of cropland.