In order to improve evaluation of e-learning systems, this paper introduces a model of learning content recommendation based on grey systems theory and gray relational analysis algorithms. This work analyses e-learners behaviours of e-learning and log files of the platform, find the most resemble e-learner, use his/her learning trail to recommend e-learning resources. The gray system theory can solve the problem of lacking historical data. And this model reach the purpose of selecting individual teaching contents intelligently.

Today's society is a society of information explosion, the popularity of the Internet and development bring a lot of convenience to people, people can easily get a lot of information on the network, however, facing so many information, people prone to the problems of "information overload" and "resources disorientation. Therefore, the recommended system came into being, the recommendation system can provide people with the most in need and most concern to avoid the time of the search and comparison. This article intends to use the very mature recommendation system in the field of electronic commerce to distance education system and promotes personalized learning, shifting the traditional "what teachers teach, what students receive" to "what the students need, what the system provides, which is consistent of constructivism study philosophy. The analysis of users interested as the basis of the recommendation system, users clustering is very important, the objective classification of fuzzy clustering analysis can recommend for users to enjoy high-quality service.