Design of an Online Teacher Training System Based on the Web and Genetic Algorithm

China faces a massive demand for quality teachers, but traditional teacher training has accessibility, customization, and scalability challenges. This paper proposes an innovative online system that utilizes web and genetic algorithms to deliver personalized and optimized training. The architecture provides remote access, intelligent content recommendation, engaging delivery, and continuous performance improvement. Detailed methodologies for learner profiling, content sequencing, and optimization engines are presented. Sample teacher data is used to showcase personalization algorithms and simulate outcomes. Results demonstrate 78% better engagement and 52% improved competency development compared to conventional methods. The system offers a framework to transform teacher qualifications at scale in China by leveraging technology-enabled, learner-centric, and outcome-focused capabilities.