Language Dissemination Paths and Modes Aided by Computer Technology

Abstract

The expansion of technology and computer science, as well as advancements in language instruction and learning methodologies, has enabled computer-assisted language learning technologies to tackle this challenge. In the field of Chinese learning, a few language learning computerized systems in the country and abroad concentrate mainly on language, grammar acquisition only have one or two assessment indicators as basis of evaluation, that definite functional flaws provide a general assessment to learners' pronunciation. In this manuscript, Language Dissemination Paths and Modes Aided by Computer Technology (LDPM-QICCNN-KOA) are proposed. The input data are collected from Chinese Corpus dataset. Then the data is given into unscented trainable kalman filter for preprocessing the input data. Then the preprocessed data are provided to QICCNN for Language Dissemination. In general, the based Quantum-inspired Complex Convolutional Neural Network doesn’t express adapting optimization approaches to determine optimal parameters to ensure exact identification. Hence, KOA utilized to enhance Quantum-inspired Complex Convolutional Neural Network, which accurately done the Language Dissemination Paths and Modes. The proposed LDPM-QICCNN-KOA method is executed on python. Then performance of proposed technique is analyzed with other existing methods. The proposed technique attains 26.36%, 20.69% and 35.29% higher accuracy; 19.23%, 23.56%, and 33.96% higher F1-Score; 26.28%, 31.26%, and 19.66% higher precision when comparing with the existing methods such as research on network oral English teaching system depend on machine learning (LDPM-DBN), nonlinear network speech recognition structure in deep learning algorithm (LDPM-DNN), research on open oral English scoring system depend on neural network (LDPM-BPNN).