Composition, color, and brushstrokes are the primary factors in evaluating and appreciating artwork and are vital points to be considered and addressed in arbitrary style transfer tasks. However, existing methods do not balance these elements well, so we propose an approach that balances content structure and style patterns for universal style transformation (BcsUST). Specifically, two lightweight encoders based on residual networks pass the extracted style and content features into the multi-domain structure attention module, which applies a self-attention mechanism to align the input features point by point followed by manifold alignment. A secondary alignment strategy is used to integrate global styles harmoniously into the semantic structure of the content, while obtaining content details to keep the structure intact. Then, the texture modulator generates style convolution parameters to dynamically adjust the filter within the micro-decoder feature subspace, injecting complex and flexible style signals for stylization to enrich the colors and form exquisite strokes. In addition, this work proposes a two-stage training strategy that introduces an artistic appreciation loss to further balance content structure and stylistic signals. Numerous qualitative and quantitative studies demonstrate that the BcsUST framework produces images that resemble the artists’ paintings more closely.