The translation of artistic style is a challenging yet crucial task for both computer vision and the arts, and the unique attributes of Chinese ink painting—such as its use of negative space, brushwork, ink diffusion, and more—present significant challenges to the application of existing style transfer algorithms. In response to these distinctive characteristics, we propose a progressive artistic aethetic ink painting style transfer method. The progressive multi-scale aesthetic style attention module in the network leverages the complementary benefits of shallow and deep style information to progressively fuse style features across multiple scales. The covariance transform fusion module addresses issues of stylistic disharmony and enhances the aesthetic quality of the style transfer while preserving the content structure effectively. Additionally, we have developed adaptive spatial interpolation module for detailed information finetuning. Finally, we conducted comparative experiments with previous studies as well as ablation studies, and invited 30 experts in art and design to perform manual evaluations. The results demonstrate that our method can achieve more aesthetically pleasing Chinese ink painting style transfers, confirming its effectiveness and artistic integrity.