Interactive Mobile English Translation Proficiency Model Based on Particle Swarm Optimisation and Neural Network for Teaching

Abstract

This paper studies an interactive mobile English translation ability analysis model based on particle swarm optimisation (PSO) and neural networks (NNs) and explores its application potential in mobile translation teaching. By integrating the global search capability of PSO algorithm and the powerful learning capability of NN, the model aims to optimise the translation quality assessment process and improve the accuracy and efficiency of translation capability analysis. By training NNs to recognise language features, style and accuracy in translated texts, and fine-tuning NN parameters with PSO algorithm, this paper constructs a model that can effectively evaluate and interactively improve mobile English translation ability. The results show that the interactive mobile English translation ability analysis model based on PSO and NN has significant teaching application value, and brings new possibilities to the field of translation education, especially mobile learning scenarios.