Learning Behavior Analysis and Prediction of Teaching System Based on Neural Network Algorithm

The current teaching system relies on students' behavior and interaction data in the system. This article proposes a teaching system based on recurrent neural networks to solve the above problems. This article collects learners' behavioral data and applies appropriate neural network models for training and analysis. Teaching systems based on neural networks can accurately capture learners' behavioral patterns and trends, and gain an in-depth understanding of their learning characteristics and behavioral preferences. Experimental results show that the accuracy of this system is 83%-94%. By analyzing learning behavior, we are able to predict learners' future learning needs and performance and provide personalized learning support. This personalized recommendation and suggestion function can help educators provide accurate learning resources and guidance, and improve learners’ learning effects and satisfaction. The research also points out the need to further improve the accuracy and interpretability of the algorithm and apply these research results to actual educational environments to achieve better teaching results. This study provides useful guidance and research basis for teaching systems based on neural network algorithms.