Existing staining normalization methods are unable to accurately extract the complex structural features of colorectal pathological images, resulting in the loss of partial structural information and the inability to generate high-quality staining-normalized colorectal pathological images. To address this issue, a staining normalization method for colorectal pathological images based on a conditional diffusion model is proposed. The proposed method includes conditional diffusion model and image feature reconstruction In conditional diffusion model，firstly, the Markov chain forward process is employed to add noise to the original colorectal pathological images. Then, the noisy images and conditional images are input into an enhanced denoising network for denoising. During this process, an enhanced activation module is utilized to learn the deep features of the colorectal pathological images and capture more contextual information. A skip-connection spatial attention module is introduced between the encoder and decoder to accurately extract the positional spatial information of the colorectal pathological images. Finally, a pyramid feature extraction module is designed to extract the features of the multi-scale conditional images and generated images, and a reconstruction loss function is constructed to optimize the performance of the entire network. Experimental results demonstrate that compared with existing methods, the proposed staining normalization method can generate higher-quality staining-normalized colorectal pathological images on public datasets GlaS and CRAG.