Research on the architecture and adaptive optimization mechanism of cultural education system in higher vocational colleges with artificial intelligence

This study focuses on the architecture and adaptive optimization mechanism of cultural education system in higher vocational colleges (HVC) integrating artificial intelligence (AI) technology. Aiming at the problems existing in the current HVC cultural education model, such as single content, insufficient interaction and weak adaptability, this paper puts forward a set of system architecture and optimization mechanism based on AI. In terms of system architecture, an intelligent cultural education system with three layers of data-algorithm-application collaboration is built. The data layer uses BERT-Transformer mixed model to build a multi-modal cultural Knowledge Graph (KG), the algorithm layer designs a two-channel collaborative filtering recommendation engine, and the application layer builds an immersive interactive interface and introduces an emotional computing model. In the adaptive optimization mechanism, a sliding window parameter optimizer is designed, and the weights are dynamically adjusted through online reinforcement learning; Establish the growth curve model of cultural literacy, and use Bayesian network to realize personalized diagnosis; Set the transparency threshold of AI decision-making to ensure educational ethics constraints. Through the one-year pilot application of students majoring in intelligent manufacturing, the results show that the experimental group is significantly better than the control group in terms of cultural resources recommendation effect and cultural education effect, and the system gradually improves the transparency and interpretability of the algorithm, which verifies the effectiveness of the system architecture and optimization mechanism, and provides theoretical support and practical path for HVC cultural education innovation.