Design of an English vocabulary e-learning recommendation system based on word bag model and recurrent neural network algorithm

Abstract

This article takes learning English vocabulary as the foundation, constructs precise classification strategies, and designs a recommendation system for English vocabulary learning. This article constructs a recommendation system for English vocabulary learning based on the word bag calculation model and recursive neural network calculation method, which has great significance, and also conducts in-depth research on this system. Thus, a classification calculation method based on the word bag calculation model was proposed. The calculation method in this paper is different from traditional calculation methods, and the classification accuracy in this paper is higher. Meanwhile, recurrent neural networks themselves have one or more feedback loops that can feed back information from the neural network to other neural networks, resulting in recurrent networks with different structures. After a series of research results, it has been found that the SIFT method takes longer to extract than the SURF method, and the number of extracted features is relatively large. Therefore, the recommendation system for English vocabulary learning proposed in this article can be improved according to the different needs of users, in order to better meet their individual requirements.