Research on intelligent recommendation system for personalized learning path based on computer deep learning

Under the current background of intelligent education, personalized learning path recommendation has become an important means to improve learning efficiency and effect. This paper discusses how to use deep learning technology to build efficient and accurate personalized learning path recommendation system. Firstly, the importance of personalized learning path recommendation and the challenges it faces are expounded. Especially in the case of rich and diverse educational resources and significant differences among students, it is particularly critical to accurately tap users' learning needs and interests and plan their learning paths scientifically and reasonably. Secondly, the application potential of deep learning model in solving this problem is introduced in detail, including using deep neural network to model user behavior, revealing hidden learning patterns through automatic feature learning, and optimizing the prediction accuracy of learning path combined with optimization methods. In the empirical research part, this paper designs and implements a personalized learning path recommendation model based on deep learning. The model comprehensively considers the user’s historical learning behavior, knowledge structure relationship and potential interest preference and other factors, and adopts multi-layer neural network architecture to conduct joint modeling and prediction, so as to realize effective recommendation for learners' personalized learning path. The experimental results show that the proposed deep learning model is superior to the traditional recommendation methods in terms of the accuracy of learning path recommendation, which verifies that deep learning has significant advantages in the field of personalized learning path recommendation.