Intelligent Teaching Methods Integrating Virtual Reality and Big Data Algorithms to Enhance Traditional Cultural...

Current conventional teaching environments feature limited interactive elements, resulting in limited engagement metrics among undergraduate populations studying cultural heritage. To address these issues, an intelligent teaching platform was developed, integrating a high-fidelity spatial rendering engine into virtual reality environments with a multi-model algorithmic architecture. Within this platform, Transformer and Neural Collaborative Filtering (NCF) models process behavioral data to generate personalized learning paths, while a Deep Q-Network (DQN) dynamically adjusts content difficulty based on real-time feedback. Such adaptive scaffolding reflects constructivist learning principles, wherein knowledge is actively constructed through interaction with contextualized, responsive environments rather than passively received through static instruction. The novelty of this research lies in integrating a spatial rendering engine with a multi-model algorithmic architecture to establish a real-time behavioral adaptation mechanism. The experimental results show that the experimental group utilizing the proposed intelligent teaching platform has higher learning scores (low/medium/high groups are 69/79/93 points, respectively) and a higher learning task completion rate (93%/96%/98%) than the control group using the traditional teaching model. The design of the intelligent teaching platform offers strong interactivity, provides personalized learning paths for students with diverse individual differences, and dynamically adjusts during the learning process. Response time analysis indicates an average latency of 0.496 s, ensuring rapid feedback on interactions. Ultimately, the proposed platform demonstrates that combining immersive VR with behavior-adaptive algorithms effectively enhances both academic achievement and task engagement for undergraduate students in traditional cultural education.