Application of Multimodal Information Fusion Technology and System Construction for Vocational Education

Modern vocational education poses new requirements for teaching models and technical support. This study addresses the challenges of processing and integrating multimodal data in vocational education by designing and implementing a multimodal information fusion teaching system. The system uses an improved SIFT algorithm and MFCC feature extraction methods for data processing, integrates features and recognition based on the Transformer architecture, and constructs a teaching platform through a microservice architecture. Experiments show that the system excels in skill action recognition, real-time interaction performance, and system stability, providing technical support for the advancement of vocational education informatization.