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研究基于粒子群算法和支持向量机的大学英语教学效果评价模型,这个模型能提高大学英语教学效果评价的准确率和效率,满足大学英语教学效果评价要求。依据相关理论构建以教师和学生为主体的多指标的大学英语教学效果评价体系,将大学英语教学效果评价指标数据作为最小二乘支持向量机输入样本,实现大学英语教学效果评价。且采用粒子群算法求解最小二乘支持向量机的核函数参数与正则化参数最优解,优化最小二乘支持向量机评价过程。实验结果表明:该方法对大学英语教学效果评价的评价效果最优,平均评价准确率高达96.56%,评价时间短,测试时间低至5 ms。

This paper studies the evaluation model of College English teaching effect based on particle swarm optimization algorithm and support vector machine which are used to improve the accuracy and efficiency of College English teaching evaluation, and meet the requirements of College English teaching effect evaluation. According to the relevant theories, a multi-index evaluation system of College English teaching effect with teachers and students as the main bodies is constructed. Taking the College English teaching effect evaluation index data as input samples, the particle swarm optimization algorithm is used to normalize the sample of the evaluation index data, and then the optimal solution is obtained by optimizing the parameters of the least squares support vector machine model, and the decision function is constructed, then the evaluation results are obtained as the output. The experimental results show that the evaluation effect of this method is the best, the average evaluation accuracy is 96.56%, the evaluation time is short, and the test time is as low as 5 ms.