Abstract: Abstract： In the research of course recommendation， the most of research effort was focused on course or video resource recommendation， only few studies paid attention to the interest or need of users for specific knowledge concept. Existing researches focus primarily on homogeneous graphs， are vulnerable to the problems of user-item relationships sparsity. To copy with the sparsity problem and fully utilize the characteristics of MOOCs datasets with multiple entities and a lot of semantic information in context relationships， a knowledge concept recommendation algorithm based on meta-path and attentional feature fusion was proposed. First， we extracted the content features of each entity and the context features between entities， input the adjacency matrices based on selected meta-paths into the graph convolutional network， and learned the representation of users and concepts under the guidance of the attention mechanism of the two-layer network structure that integrated the feature vectors of the meta-path and potential feature vectors of users and concepts. Finally， these learned user and concept representations were incorporated into an extended matrix factorization framework to predict the preference of concepts for each user. Experimental results on MOOCCube dataset demonstrate that the algorithm attains the best hit rate， the best normalized discounted cumulative gain and the best mean reciprocal ranking than those of BPR， FISM， NAIS， Metapath2vec， and MOOCIR algorithms. The algorithm improves the interpretability and prediction accuracy of the recommendation process to a certain extent， and alleviates the problem of user-item relationships sparisty.

Key words: Key words： concept recommendation, matrix factorization, attention mechanism, meta-path, heterogeneous information network