The information industry is developing rapidly and the mainstream digital media has produced an evolution from text to pictures to videos. How to quickly and effectively extract the key points of interest of characters in videos has become a hot topic in the field of Internet entertainment and big data analysis. However, existing methods for acquiring character information usually have significant limitations in obtaining information directly from the video interface. To address this problem, this paper proposes a novel “coarse to fine” intelligent face search framework based on feature hybrid clustering and key point detection. The real-time search of face data under big data is subdivided. First, the face detection algorithm based on multi-scale depth feature hybrid clustering uses the Softmax function to achieve data classification, and then uses the central loss function to form clustering centers that are modified by the regression of centroids to achieve coarse screening of faces. Then, based on the face key point detection algorithm, 68 individual face key feature points are extracted to generate standardized features that are easy to calculate and process to realize the fine search of faces under big data. This enables real-time and highly robust intelligent face search from Internet video data. Notably, this paper also constructs two film and television face datasets to provide big data analysis for subsequent related Internet industry and entertainment multimedia. System’s overall experimental results prove that this paper has a certain improvement in recognition accuracy and efficiency compared with existing mainstream face detection methods, including a 31.2% improvement in recognition efficiency and 3 times improvement in discrimination of false-positive samples, and the overall operation efficiency meets the standard and has certain practical value.