Fully automatic camera movement directly affects the art quality of dance expressiveness, especially in terms of visual expression, as well as choreography and music. Current studies mainly focus on synthesizing camera movements conditioned on dance and music, but they overlook the camera movement style, which is essential factor for artistic and visual coherence. In this paper, we introduce DanceStyleCam, a unified framework that incorporates the style-consistent characteristic into dance camera movement synthesis with diverse stylistic characteristics. Specifically, a style-aware feature learning module is proposed to map dance style information into compact embeddings, facilitating stable and discriminative style learning. To further guarantee that the generated camera movements remain faithful to the target style, we propose a style-consistent adversarial training scheme, leading and optimizing the model to learn better style-consistent representations. In addition, we also enrich the DCM dataset with diverse camera movement style annotations. Extensive experiments demonstrate that DanceStyleCam outperforms state-of-the-art methods in both generation quality and style consistency. Project page: https://github.com/YCCCCM/DanceStyleCam.