Unsupervised face attribute editing methods based on the latent space of Generative Adversarial Networks （GANs） offer advantages of high efficiency and only label-free data required， but they still face challenges in terms of decoupling and controllability， for instance， modifying a specific face attribute may alter other attributes inadvertently， thereby affecting editing quality， and precise control over the degree of attribute modification remains difficult. To address these issues， a dynamic convolutional Autoencoder-based Unsupervised Face Attribute Editing （AUFAE） method was proposed to achieve precise face attribute editing by learning effective semantic vectors in the latent space. Specifically， a Dynamic Convolutional AutoEncoder Network （DCAE-Net） was designed as the backbone， where Dynamic Convolution （DyConv） was utilized by the encoder to extract local latent-space features adaptively， thereby learning semantic vectors with local characteristics. A Channel Attention （CA） mechanism was incorporated into the decoder to establish nonlinear dependencies between channels， thereby allowing the model to focus on feature channels relevant to different semantics autonomously and enhancing the independence of semantic vector learning. To improve decoupling and controllability of semantic vectors， an attribute boundary vector-based loss function was introduced to train DCAE-Net. Additionally， a soft orthogonality loss was applied to ensure mutual independence of semantic vectors， thereby further boosting decoupling performance. Experimental results show that on three pre-trained GAN generation models， compared with three mainstream face attribute editing methods， AUFAE has the Fréchet Inception Distance （FID） decreased by 37.43%-50.21%， the Learned Perceptual Image Patch Similarity （LPIPS） decreased by 23.61%-42.85%， and the Structural Similarity Index Measure （SSIM） increased by 7.04%-13.42%. On intuitive vision， AUFAE does not exhibit attribute coupling during face attribute editing process. It can be seen that AUFAE can alleviate the attribute coupling in face editing process effectively and achieve more accurate face attribute editing.