Intelligent education platform for industry-education integration: technical architecture and key algorithms

The rapid development of AI technology has addressed the issue of how to align academic curricula with the ever-changing demands of the industry. This has had a significant impact on vocational and technical education. This paper proposes an intelligent curriculum design framework to bridge the gap between academic courses and the ever-changing skill demands of the industry. Using artificial intelligence technology, the platform achieves dynamic and secure integration of industry education. The platform integrates a hybrid recommendation engine, LSTM-based demand forecasting, and privacy-preserving federated learning systems. The hybrid recommendation engine addresses the cold start problem and improves course matching accuracy through collaborative filtering and knowledge graph embedding. The demand forecasting engine achieves real-time course adjustments by analyzing temporal patterns in industry data to reduce delays. Federated learning can ensure that institutions collaborate securely without compromising data privacy. The VED Tech 2025 dataset, which includes 78,000 industry skill requirements and over 1.2 million student course records, shows that the platform's skill gap has decreased by 41%, course relevance has increased by 23.7%, and the median adaptation delay is 3.2 days. These findings highlight the platform's potential in enhancing graduates' employability, reducing corporate training costs, dynamically aligning academic services with industry needs, and providing a scalable, secure, and data-driven solution for integrating high-quality industry education.