摘要 : In order to overcome the problems of time-consuming and high recommendation error in traditional public English MOOC teaching resource recommendation methods, this paper proposes a new public English MOOC teaching resource recomme... 展开 In order to overcome the problems of time-consuming and high recommendation error in traditional public English MOOC teaching resource recommendation methods, this paper proposes a new public English MOOC teaching resource recommendation method based on data partition. The data of public English MOOC teaching resources are collected, and hierarchical clustering algorithm is used to preprocess public English MOOC teaching resources to support the recommendation demand of mobile MOOC teaching resources. According to the preprocessing results, the data block algorithm is used to divide the resource data iteratively. Finally, we calculate the similarity of users' resource use and preference, and construct the public English MOOC teaching resource recommendation model based on the index weight results. Comparative validation results show, in the conventional method, the proposed method recommended consuming less and less precision compared to the recommended. 收起