Design and optimization of education informatization model and intelligent recommendation system based on student perception

The study aims to design and optimize an intelligent education informatization model and recommendation system based on student perception, utilizing a novel Customized Butterfly-Optimized Feed-Forward Neural Network (CBO-FFNN). This research employs a hybrid AI-based architecture that combines butterfly optimization with feed-forward neural networks to enhance recommendation accuracy. The system integrates user perception data, borrowing patterns, and course-specific learning outcomes to tailor educational content recommendations. A dataset comprising 42,153 users, 70,000 books, and over 200,000 borrowing records (2021–2024) was analyzed. Additionally, a survey was conducted with 3,516 students enrolled in programming courses, of whom 3,000 responses were considered valid for evaluating satisfaction and system usability. Experimental results demonstrate that the proposed CBO-FFNN model significantly outperforms traditional methods (TF-IDF, CF, and Improved TF-IDF) in accuracy (up to 91.4%), precision (92.1%), recall (92.5%), and F-value (95.7%) across various feature dimensions. Moreover, satisfaction analysis shows over 90% of students found the system useful, relevant to their learning needs, and easy to use. The system provides educational institutions with a robust framework for adaptive, perception-aware content delivery, enabling personalized learning and data-driven curriculum refinement. This is the first application of a butterfly-optimized neural model tailored to education informatization, integrating both system performance and perceptual feedback to enhance educational recommendation systems.