Research on Data Retrieval Algorithm of English Microlearning Teaching Based on Wireless Network Information Classification

Abstract

This paper introduces a data retrieval algorithm for teaching English microlearning based on the classification of wireless network information. There are two main types of information extracted from social network information: trust relationship and similarity relationship. To be able to make full use of these two kinds of information, they are then divided into two parts, respectively, namely, explicit and implicit trust relationships and global and local similarity relationships. Then, this paper proposes an adaptive adjustment of the weights, which can better model the user’s selection tendency. Finally, adequate experiments are conducted on two experimental data sets, and the retrieval model shows the best results, demonstrating that the impact of data sparsity on retrieval performance can be mitigated through the use of social network information. The general approach to the production of college English microcourse is described in terms of design principles, teaching analysis, teaching session design, script design, and recording processing, and the study of data retrieval algorithms for college English microcourse based on social network information classification is conducted in three stages: before, during, and after the class. It is verified through practice that the application of social network information classification to college English microlearning helps to improve learning interest, learning efficiency, independent learning ability, and thinking inquiry ability and provides certain teaching suggestions for college English microlearning based on practical feedback.