For the monotonicity of learning resources recommended by the collaborative filtering algorithms may lead to the difficulty of meeting the need of personalized resource acquisition of learners， a recommendation algorithm for online learning resources based on double-end neighbor fusion knowledge graph was proposed. Firstly， on the user end， the information of the entities and their neighbors between the learner’s existing knowledge nodes and the new knowledge nodes were aggregated to obtain the embedding representation of the learner in order to capture the learner’s personalized requirements. Secondly， on the project end， the neighborhood information of learning resources was used to expand the semantics and embedding representation of the learning resources. Finally， the user embedding representation and the project embedding representation were sent to the fully connected layer to obtain the interaction probability of them. To verify the effectiveness of the proposed algorithm， comparison experiments were performed using the public dataset MOOPer. Experimental results show that on this dataset， the proposed algorithm improves 1.12 percentage points and 1.31 percentage points on AUC （Area Under Curve） and accuracy respectively compared to the optimal baseline model， and achieves certain improvement on both of Precision@K and Recall@K.