Abstract:Personalized service is the inherent requirement and key point in building an intelligent learning environment. The utilized probability of learning resources can be improved by pushing algorithm for main body (learner) of the learning environment, and then can solve the problem that learners easily lose when they are studying on-line. The internal structure characteristics of the learners and learning resources are established through the unity semantics based on knowledge ontology, then a recommendation algorithm which combines the time attenuation function and difficulty matching method is designed to effectively calculate the correlation between them. The time attenuation function expresses the time-ordered behaviors of the learners in order to reflect the knowledge migration feature, and the difficulty matching method matches with learners' cognitive level and resource's difficulty. Finally, experimental results show that the time attenuation function and difficulty matching method reach the expected target and can guarantee the quality of personalized learning resources recommendation better in their common effect.