Face forgery detection (FFD) field has found significant interest from the scientific community. It is noted that forged images often have subtle inconsistencies in stylistic changes (such as background illumination, color luminance) and unnatural textures (such as edge visual artifacts), critical for identifying subtle manipulations. Therefore, we divide a forged image into two views: one for the texture and one for the background style, and develop a Style-Texture Collaborative Learning (STCL) for Face Forgery Detection to enhance the subtle tampering clues by jointly considering the inconsistency of texture and style from the input image. Specifically, we apply a pair of style-texture editors to excavate style and texture information separately. Then, we introduce a style-texture relation modeling mechanism to bridge the correlations between the two views from whole visual. Finally, we present a multi-branch sequence analysis module to progressively accumulate tampering changes by ladder-scanning larger areas for novel regions exhibiting similar characteristics through multiscale sequence blocks. Extensive experimental evaluations conducted on three widely recognized datasets demonstrate the effectiveness of our proposed method compared to state-of-the-art approaches.