School of Foreign Studies, Suqian University, Suqian 223800, Jiangsu, China.
School of Education, Taylor's University, Subang Jaya 475002, Selangor, Malaysia.
Comput Intell Neurosci. 2022 Oct 5;2022:8955638. doi: 10.1155/2022/8955638. eCollection 2022.
In order to solve the intelligent evaluation of English writing, this paper proposes a method based on the English semantic neural network algorithm. This paper first briefly analyzes the research background of the English semantic analysis system, then expounds on the relevant technologies of the English distance similarity algorithm, semantic analysis intelligent algorithm structure, word analysis algorithm, sentence part of speech analysis algorithm, sentence semantic analysis algorithm, and neural network algorithm, and finally expounds the database and method implementation of the English semantic analysis system, so as to provide guarantee for the design of the English semantic analysis system. The experimental results show that the recognition accuracy of the BRF network for English characters can reach 96.35%, which is 7.79% higher than that of the BP network; the AUC of the BRF network reaches 0.89, which is closer to 1 compared with 0.72 of the BP network. The test results are in good agreement with the antinoise curve test results of the figure. It is proved that the English semantic neural network algorithm can effectively improve the accuracy of English translation and further improve the efficiency of the system.
为了解决英文写作的智能评估问题，本文提出了一种基于英文语义神经网络算法的方法。本文首先简要分析了英文语义分析系统的研究背景，然后阐述了英文距离相似性算法、语义分析智能算法结构、词分析算法、句子词性分析算法、句子语义分析算法和神经网络算法的相关技术，最后阐述了英文语义分析系统的数据库和方法实现，为英文语义分析系统的设计提供了保障。实验结果表明，BRF 网络对英文字符的识别准确率可达 96.35%，比 BP 网络高 7.79%；BRF 网络的 AUC 达到 0.89，与 BP 网络的 0.72相比更接近 1。测试结果与图的抗噪声曲线测试结果吻合较好，证明了英文语义神经网络算法可以有效提高英文翻译的准确性，进一步提高系统的效率。
