In recent years， Generative Adversarial Network （GAN） used for Low-Dose Computed Tomography （LDCT） image denoising has shown significant performance advantages， becoming a hot topic in the field. However， the insufficient perception ability of GAN generator for the noise and artifact distribution in LDCT images leads to limit the denoising performance. To address this issue， an LDCT denoising model based on a Dual Encoder-Decoder GAN （DualED-GAN） was proposed. Firstly， a pair of encoder-decoder was proposed to form an artifact pixel-level feature extraction channel for estimating the artifact noise in LDCT images. Secondly， another pair of encoder-decoder was proposed to form an artifact mask information extraction channel for estimating the intensity and location information of artifacts. Finally， the artifact image quality label maps were used to assist in estimating the mask information of artifacts， so that supplementary features were provided for the artifact pixel-level feature extraction channel， thereby enhancing the sensitivity of the GAN denoising network to the distribution intensity of artifact noise. Experimental results show that compared with the sub-optimal model DESD-GAN（Dual-Encoder-Single-Decoder based Generative Adversarial Network）， the proposed model increases the average Peak Signal-to-Noise Ratio （PSNR） by 0.338 7 dB， and the average Structural Similarity Index Measure （SSIM） by 0.002 8 on mayo test set. It can be seen that the proposed model performs better in all terms of artifact suppression， structural preservation， and model robustness.