In the context of the popularization and diversified application of information technology in higher education, efficient information dissemination has a significant impact on the learning effect of the learning community. Improving the efficiency of information dissemination and driving the force of learning to enhance the learning effect are the hot issues in the field of higher education data analysis. This paper proposes a new method of feature fusion using information entropy and ReliefF algorithm, applies the improved PageRank algorithm and K-means algorithm to optimize the information transfer mode, and finally develops a new and efficient network information model. The comparative test results show that the new model can complete the dissemination of the same amount of information with a smaller delivery ratio. The research results can play an advantageous role in information interaction feedback, curriculum quality analysis, and teaching information transmission.

中文翻译：

---

基于学习共同体的大学生教育改善信息传播模式优化

在信息技术在高等教育中普及和多样化应用的背景下，高效的信息传播对学习共同体的学习效果有着显着影响。提高信息传播效率，驱动学习动力，提升学习效果，是高等教育数据分析领域的热点问题。本文提出了一种利用信息熵和ReliefF算法进行特征融合的新方法，应用改进的PageRank算法和K-means算法来优化信息传递方式，最终开发出一种新的高效的网络信息模型。对比测试结果表明，新模型能够以较小的传递率完成相同信息量的传播。