Transfer learning and knowledge graph enhanced VR animation resource recommendation with creativity prediction

Virtual reality technology has transformed animation education by providing immersive learning environments, yet the proliferation of VR-based teaching resources presents significant challenges in resource discovery and personalized learning design. This research proposes an intelligent recommendation system that integrates transfer learning and knowledge graph technologies to address cold-start and data sparsity challenges in VR animation education. The system comprises two core components: a hybrid recommendation engine combining transfer learning algorithms with knowledge graph reasoning to generate context-aware resource suggestions, and a creativity development path prediction model based on LSTM-attention mechanisms that analyzes learning behaviors and forecasts individualized development trajectories. A comprehensive knowledge graph for VR animation teaching was constructed to capture domain concepts, resource attributes, and pedagogical relationships. Experimental results demonstrate substantial improvements over baseline methods across multiple metrics, with the proposed system achieving optimal performance in precision, recall, F1 score, and NDCG. Deployment in authentic educational settings yielded measurable gains in student learning outcomes and creativity competency development, validating the system's practical effectiveness in enhancing personalized animation education through intelligent resource recommendation and proactive pedagogical interventions.