This paper presents a novel approach to generating artistic portraits from face photos using feature disentanglement and reconstruction. The method integrates six key components: a U-Net-based image generator, an image discriminator, a feature-disentanglement module, a feature-reconstruction module, a U-Net-based information generator, and a cross-modal fusion module. These components work together to transform face photos into artistic portraits that retain the subject's identity and expressiveness while incorporating diverse artistic styles. The feature-disentanglement module uses wavelet transform to extract content features from the face photo, while the feature-reconstruction module merges these features with style information to generate the final portrait. Extensive experiments on the APDrawing dataset demonstrate superior performance in visual quality, achieving a significant reduction in the Fréchet Inception Distance (FID) score to 61.23. Ablation studies further validate the effectiveness of each component in enhancing the artistic quality of the generated portraits. The proposed method outperforms existing techniques in generating more realistic and diverse artistic portraits, making it a significant advancement in the field of image generation.This paper presents a novel approach to generating artistic portraits from face photos using feature disentanglement and reconstruction. The method integrates six key components: a U-Net-based image generator, an image discriminator, a feature-disentanglement module, a feature-reconstruction module, a U-Net-based information generator, and a cross-modal fusion module. These components work together to transform face photos into artistic portraits that retain the subject's identity and expressiveness while incorporating diverse artistic styles. The feature-disentanglement module uses wavelet transform to extract content features from the face photo, while the feature-reconstruction module merges these features with style information to generate the final portrait. Extensive experiments on the APDrawing dataset demonstrate superior performance in visual quality, achieving a significant reduction in the Fréchet Inception Distance (FID) score to 61.23. Ablation studies further validate the effectiveness of each component in enhancing the artistic quality of the generated portraits. The proposed method outperforms existing techniques in generating more realistic and diverse artistic portraits, making it a significant advancement in the field of image generation.