Interactivity of English online learning based on neural network

Abstract

The operation of the mobile interactive autonomous learning system injects advanced management technology and scientific and reasonable management system for the effective management and arrangement of the school's teaching, enables students to conduct autonomous learning in their spare time, makes full use of students' fragmented time, and improves students' learning enthusiasm and efficiency. Based on the neural network algorithm, this paper constructs an English network learning interactive evaluation model, studies the influence of the flattening layer in the convolutional neural network structure on the structural complexity, and then proposes spatial pyramid weighted average pooling to optimize the traditional flattened layer. Moreover, this paper evaluates RSPP from three aspects: space complexity, training time-consuming, and accuracy. In addition, this paper designs two types of integrated convolutional neural networks, namely Bagging integration and snapshot integration. Finally, this paper evaluates the performance of the model through experiments and counts the experimental results. The research results show that the model constructed in this paper has a certain effect.