Optimizing Information Dissemination Model for Improvement of College Students’ Education Based on Learning Community

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.