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基于IGWO-SVM的大学英语跨文化交际能力评价
引用本文:王娅. 基于IGWO-SVM的大学英语跨文化交际能力评价[J]. 现代科学仪器, 2021, 0(1): 145-150
作者姓名:王娅
作者单位:陕西铁路工程职业技术学院
基金项目:“陕西铁路工程职业技术学院科学研究基金项目”(项目编号:KY2019-70)成果。
摘    要:为实现大学英语跨文化交际能力的精准评价,改善英语教学对跨文化交际方面的教学困境。本文建立了首先引入最佳点集和随机分布收敛因子改进的GWO算法,并综合运用层次分析法从交际能力和跨文化能力2个方面构建英语跨文化交际能力评价指标体系,最后,大学英语跨文化交际能力评价二级指标的得分数据和能力水平分别作为SVM的输入向量和输出向量,建立了基于IGWO-SVM的大学英语跨文化交际能力评价模型。IGWO-SVM模型的大学英语跨文化交际能力评价具有更高的准确率和更快的收敛速度且具有更高的稳定性,为大学英语跨文化交际能力评价提供了新的方法。

关 键 词:跨文化交际  层次分析法  评价体系  灰狼优化算法  支持向量机

Evaluation of College English Intercultural Communicative Competence Based on IGWO-SVM
Wang Ya. Evaluation of College English Intercultural Communicative Competence Based on IGWO-SVM[J]. Modern Scientific Instruments, 2021, 0(1): 145-150
Authors:Wang Ya
Affiliation:(Shaanxi Railway Engineering Vocational and Technical College,Weinan,Shaanxi 715400)
Abstract:In order to realize the accurate evaluation of college English intercultural communication ability,improve the teaching dilemma of intercultural communication in English teaching.This paper establishes an improved GWO algorithm that first introduces the best point set and random distribution convergence factors,and comprehensively uses the analytic hierarchy process to construct an English intercultural communicative competence evaluation index system from two aspects:communicative competence and cross-cultural competence.Finally,college English cross-cultural competence The score data and ability level of the secondary indicators of cultural communication ability evaluation are used as the input vector and output vector of SVM respectively,and an evaluation model of college English cross-cultural communication ability based on IGWO-SVM is established.The IGWO-SVM model of college English intercultural communicative competence evaluation has higher accuracy,faster convergence speed and higher stability,which provides a new method for college English intercultural communicative competence evaluation.
Keywords:intercultural communication  analytic hierarchy process  evaluation system  grey wolf optimization algorithm  support vector machine
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