Optimization of a Business English Tutoring System Based on Intelligent Recommendation Algorithms and Multimodal Data Analysis

To address the issue of low learning efficiency in listening, speaking, and viewing among English learners, an optimized tutoring system solution based on intelligent recommendation algorithms and multi-modal data analysis has been proposed to meet the personalized needs of resource recommendations. The system primarily constructs an adaptive learning and learning style analysis model, enhancing the accuracy of user feature extraction through multi-modal data fusion. By combining clustering algorithms to optimize the recommendation process, the accuracy of user similarity calculations within the system has been further improved. In terms of system architecture design, it includes layers for learning users, business logic, and data processing, employing a layered architecture technique to ensure efficient data transmission while maintaining the real-time responsiveness of the system. Experimental results indicate that the system has achieved significant improvements in user experience and recommendation accuracy through optimization, particularly demonstrating higher stability in the correlation analysis between user learning behaviors and visual perception feedback. This study provides a technical reference for the personalized design and intelligence of other related tutoring systems.