<p>The aim of arbitrary style transfer is to render an image with the style of a reference image while preserving the original content. A key challenge is the extracting and maintaining global image information due to the local nature of convolutional neural networks (CNNs). Therefore, arbitrary style transfer methods incorporate a limited representation of the content. Furthermore, incoherence in local style transfer also contributes to the generation of low-quality images. In order to significantly reduce content bias and generate high-quality images, we introduce a novel arbitrary style transfer approach named RECDFS (Rotation Equivariant Convolution and Delayed Feature Statistics). In detail, we propose a Consistent Neural Content Mapping (CNCM) module based on Rotation Equivariant Convolution and attention mechanism. CNCM enhances feature extraction and attends to key-style representations. Based on CNCM, we design a delayed global feature statistics (DGFS), which is used to align the second-order statistics of content and style features. Additionally, we design two loss functions to optimize detail enhancement. Importantly, our method achieved state-of-the-art results compared with twelve classic methods.</p>