Cultivation Strategies of English Thinking Ability in the Environment of Internet of Things

Abstract

The study aims to broaden the horizons of English learners and solve the problem of insufficient cultivation of English thinking. With the widespread use of the Internet of Things (IoT) and from the perspective of deep learning, the Local Similar Convolutional Neural Network (LSNN) recommendation model is designed by adding adjustment layers to the Convolutional Neural Network (CNN). The LSNN model alleviates the sparsity of data. Through comparative experiments on related data, the data sparsity is 0.7-0.9. The LSNN prediction is higher than that of Euclidean Distance and Pearson correlation coefficient, which proves that LSNN can alleviate data sparsity. The LSNN model has the lowest mean absolute error (MAE) of 0.83, which is smaller than the previous CNN’s MAE. The LSNN model recommends the expansion books they need most for English learners, and then improves the vision of English learners, thereby strengthening the cultivation of English learners’ thinking ability.