The use of low-dose CT scans has significantly reduced radiation exposure during examinations. However, this reduction has led to increased noise and artifacts in CT images, compromising image quality and diagnostic accuracy, which can affect physicians′ judgment during the diagnostic process. Recent advancements in generative models have demonstrated excellent performance in addressing these issues. Nonetheless, these models still face challenges, such as generating confusion and structural deficiencies during the generation process. To tackle these problems, a conditional diffusion denoising network model has been developed. This model incorporates a trainable curve matching module to correct different noise levels and includes a joint loss function. Experimental results indicate that the proposed algorithm achieves superior denoising outcomes compared to comparative algorithms, with test results of PSNR 35.70 and SSIM 0.912 8 on the dataset, representing the optimal performance among the selected methods. Additionally, it demonstrates good generalization across low-dose CT images with varying radiation doses, maintaining excellent denoising levels.