On the MOOC platform， there may be multiple versions of videos for one course，in order to recommend a MOOC video that satisfies the learning interest of the student，it is necessary to analyze the relationship between student interests and video contents. For this purpose， a video recommendation model named Mrec was proposed based on meta-path attention mechanism. For one thing， the Heterogeneous Information Network （HIN） was used to describe the relationships between learners and MOOC resources， and then meta-path was used to express the interaction between students and videos. For another， the attention mechanism was used to capture the influences of the characteristics of students， videos and meta-paths on learning interest. Specifically， the Mrec model was composed of two layers of attention mechanism： the first layer was the node attention layer， the node own characteristics were weightely combined with neighbor characteristics， and the feature representations of entities under different meta-paths were obtained by multi-head attention； the second layer was the path attention layer， in which the feature representations of entities learned under the guidance of different meta-paths were integrated to capture the feature representations of entities under different interests， and the learned users and video entities were put into Multi-Layer Perceptron （MLP） to obtain the prediction scores for top-K recommendation. Experimental results on MOOCCube and MOOCdata datasets show that Mrec outperforms the comparison methods in terms of Hit Ratio （HR）， Normalized Discounted Cumulative Gain （NDCG）， Mean Reciprocal Ranking （MRR） and Area Under receiver operating characteristic Curve （AUC）.