In view of the problem that the traditional learning service recommendation does not fully consider the distinct differences between individuals, it is easy to lead to the contradiction between unchanging learning resources and learners’ personalized learning needs that are constantly improving, so an adaptive learning service recommendation improvement algorithm based on big data is proposed. Idea is based on adaptive learning platform and function modules. We consider the individual differences between students, to students as the center, collect students’ personalized learning demand data, and according to the data information to build student demand model. On the basis of using data mining methods for clustering recommendation service resources in learning, the adaptive recommend according to students’ individual need is proposed. The experimental results show that the adaptive learning service recommendation algorithm based on big data has high recommendation accuracy, coverage rate and recall rate, which is of great significance in the actual learning service recommendation.

鉴于传统学习服务推荐没有充分考虑个体之间的明显差异，很容易导致学习资源不变和学习者的个性化学习需求不断提高之间的矛盾，因此自适应学习服务推荐提出了一种基于大数据的改进算法。创意基于自适应学习平台和功能模块。我们考虑学生之间的个体差异，以学生为中心，收集学生的个性化学习需求数据，并根据这些数据信息建立学生需求模型。在运用数据挖掘方法对学习中的推荐服务资源进行聚类的基础上，提出了根据学生个人需求的自适应推荐。