With the rapid evolution of AI and human-computer interaction technologies, Chinese character animation has gained wide applications in film/TV effects, digital education, and cultural heritage digitization. Current methods relying on static datasets face critical limitations in generation efficiency, style diversity, and real-time responsiveness. We address these challenges through an intelligent animation system incorporating three interconnected innovations: a dynamic dataset architecture supporting real-time updates for over 3,000 characters, a style-adaptive character description library, and a decoupled compilation-rendering framework that independently manages content generation and visual execution. By integrating stroke feature extraction with stroke-order reconstruction algorithms, our system automatically converts input characters into customizable animations with parametric control of curve smoothness and motion dynamics. Experimental validation confirms substantial efficiency improvements over conventional approaches, coupled with robust cross-platform compatibility and enhanced interactive capabilities across diverse usage scenarios. This work establishes a new paradigm for dynamic dataset-driven character animation systems.