Scene text editing is widely used in various fields, such as poster design and correcting spelling mistakes in the image. Editing text in images is a challenging task that requires accurately and naturally integrating text within complex backgrounds. Existing methods have achieved changing the text content with the target text without altering the style of text and the background of the image. However, arbitrary style transformation of the text region in the image has not been achieved. To address this issue, we propose a new framework named FontCLIPstyler, which enables the style transformation of text in scene text images using prompts. The proposed method mainly comprises two networks: MaskNet, which extracts mask images of the text region in images, and StyleNet, which performs the generation of stylized images. In addition, we also propose a new loss function named Text-aware Loss, which can guide the StyleNet network in transferring style features to the text region without changing the background. Through extensive experiments and ablation studies, we have demonstrated the effectiveness of our method in scene text style transformation. The experimental results show that our approach can successfully transfer the semantic style from the input prompt to the text region of the image, and create naturally stylized scene text while keeping the readability of the text and the background invariant.