A study on the personalized learning path recommendation of intelligent learning system based on user behavior characteristic analysis

At present, the rapid development of Internet information technology has brought convenience to education and teaching. E-learning based on network has become an important way of learning. However, the traditional E-learning learning environment usually only provides course learning resources by the fixed preset program, which is not friendly to the user's personalized learning experience. Intelligent learning system (ILS) is a support environment that can dynamically provide personalized and intelligent learning services by diagnosing the individual differences of learners' users. It mainly provides current users with suitable learning paths or learning resources via personalized recommendation technology, so as to meet users' learning needs and improve their learning experience. However, the current research on intelligent learning system is still in the exploratory stage. Many researchers have studied intelligent learning system from different perspectives, but their research achievements still need to be explored and improved on the effect of intelligent recommendation. Based on this, this paper will further explore the key technology of personalized recommendation of intelligent learning system on the basis of the related research case analysis. In order to improve the recommendation accuracy of intelligent learning system, we will model and analyze the user behavior characteristics, and further explore the recommendation solution of personalized learning path of the system. The simulation experiment shows that the research results have a good personalized recommendation effect, which can effectively diagnose and analyze the current user's learning state characteristics, and then dynamically provide a suitable personalized learning path to meet the user's learning needs.