Design and Application of Intelligent Online Learning System Based on Machine Learning Algorithm

The exponential expansion of digital infrastructure over recent years has significantly propelled the global integration of e-learning platforms. However, conventional educational platforms face inherent limitations including inflexible curriculum structures and inadequate personalized assessment mechanisms, which hinder their ability to address learners’ multifaceted requirements. To overcome these challenges, this research develops an AI-driven e-learning architecture incorporating machine learning methodologies. The proposed system integrates a learning process optimization engine and an intelligent diagnostic feedback system, leveraging reinforcement learning techniques to establish a self-adaptive learning trajectory optimization framework. This enables streamlined learning process administration, precise competency evaluation, and generation of customized learning pathways through dynamic adjustment mechanisms. The experimental data shows that 60% of the control group scores 80-89 points, 40% scores 90-100 points, while 35% of the experimental group scores 80-89 points, and 65% scores 90-100 points. This shows that the system can significantly enhance the learning efficiency and effect of learners, effectively solve the inherent defects of traditional online learning systems, and provide new ideas and methods for the development of intelligent online learning systems.