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MOOCs might be an important organization way to realize the online learning process. Online technology and sharing technology enable MOOCs to realize the adaptive scheduling of learning resources, as well as the independent construction of learning sequences. At the same time, it also generates a large number of complex learning behaviors. How to mine and predict the value and laws of MOOCs is a difficult problem in learning analytics applied to MOOCs. This study integrates the context information of the learning process of MOOCs, designs a deep learning model based on multi-entity knowledge graph, in order to predict and track the interest propagation of learning behaviors, and realizes the learning trend guidance mechanism supported by multi features and complex relationships. Through sufficient experiments, the effectiveness and reliability of this model are verified, and based on the analysis results, the best path and implementable scheme for the interest propagation of learning behaviors are constructed. The whole research might provide more references for the study of learning behaviors described by multi-entity knowledge graph of MOOCs.

Interactive learning is a two-way learning method of learners independently by using computer and network technology. In the interactive relationships, interactive learning plays a role for learners to achieve the learning purpose, interactive learning has become an important effect of online learning, but it also has many problems that need to be improved. In particular, how to apply the massive data to achieve data-driven early warning needs further research. This study firstly mines the temporal series of learning behavior features, the corresponding data is collected from one online learning platform of The UK Open University, the massive data obtained from the platform have been desensitized, disclosed and shared, which might ensure the comparison and verification of research results. Secondly, we design a early warning mechanism based on temporal memory enhancement model. Through a large number of data training and testing, this method is useful to the analysis of learning behavior features, and has strong effectiveness and reliability. Thirdly, the decision and intervention mechanism are mined and predicted. The whole work is of great significance to the early warning mechanism of interactive learning process, which has strong theoretical value and practical significance.