At present， the grayscale levels of electrowetting electronic paper are not enough to meet the display needs of mainstream images. Ink reflow also affects the image quality on electrowetting electronic paper， making it difficult to distinguish details， and leading to issues such as edge blur， loss of image details， and a strong particle sense in the displayed image. In order to solve these problems， this paper proposed an adaptive error diffusion algorithm based on characteristics of the grayscale information distribution in images. First， in order to obtain a high-fidelity grayscale reference image with 256 levels， an input color image was denoised and grayscale processed. Second， we employed sub-pixel edge detection to meticulously segment the image into edge and smooth regions. This method emphasized the edge areas while simultaneously enhancing the texture of smooth regions. This enhancement was tailored to align with the local visual characteristics of human eyes by considering the grayscale variations within the neighborhood of image. Last but not least， we adaptively adjusted the diffusion error threshold according to the grayscale information distribution characteristics of the reference image. This method reduced the error between the original reference grayscale image with 256 levels and the processed image. The experimental results showed that the images processed by this algorithm get 4.227 8 in the mean opinion score of the five-grade scoring system. The average peak signal to noise ratio reached 45.903 3 dB， marking a 16.47% to 48.89% improvement over other advanced technologies. Additionally， the average visual information fidelity reached 0.964 3， marking a 1.53% to 50.02% enhancement compared to other advanced methods. The method presented in this paper makes the edges of the images displayed on electrowetting electronic paper clearer and improves the detail and texture， thereby enhancing the readability of the electrowetting electronic paper.