In this paper, the chaotic neural network model of big data analysis is used to conduct in-depth analysis and research on the English translation. Firstly, under the guidance of the translation strategy of text type theory, the translation generated by the machine translation system is edited after translation, and then professionals specializing in computer and translation are invited to confirm the translation. After that, the errors in the translations generated by the machine translation system are classified based on the Double Quantum Filter-Muttahida Quami Movement (DQF-MQM) error type classification framework. Due to the characteristics of the source text as an informative academic text, long and difficult sentences, passive voice, and terminology translation are the main causes of machine translation errors. In view of the rigorous logic of the source text and the fixed language steps, this research proposes corresponding post-translation editing strategies for each type of error. It is suggested that translators should maintain the logic of the source text by converting implicit connections into explicit connections, maintain the academic accuracy of the source text by adding subjects and adjusting the word order to deal with the passive voice, and deal with semitechnical terms by appropriately selecting word meanings in postediting. The errors of machine translation in computer science and technology text abstracts are systematically categorized, and the corresponding post-translation editing strategies are proposed to provide reference suggestions for translators in this field, to improve the quality of machine translation in this field.

中文翻译：

---

基于大数据分析的英语机器翻译混沌神经网络模型

本文利用大数据分析的混沌神经网络模型对英语翻译进行深入的分析和研究。首先，在文本类型理论翻译策略的指导下，对机器翻译系统生成的译文进行翻译后的编辑，然后邀请计算机和翻译领域的专业人士对译文进行确认。之后，基于双量子滤波器-Muttahida Quami Movement (DQF-MQM)错误类型分类框架对机器翻译系统生成的翻译中的错误进行分类。由于源文本作为信息性学术文本的特点，长难句、被动语态、术语翻译是机器翻译错误的主要原因。鉴于源文本的严谨逻辑和固定的语言步骤，本研究针对每种类型的错误提出了相应的译后编辑策略。建议译者应通过隐性连接转为显性连接来保持原文的逻辑性，通过添加主语和调整语序处理被动语态来保持原文的学术准确性，处理半专业术语时应采用在发布编辑时适当选择单词含义。对计算机科学与技术文本摘要中机器翻译的错误进行系统分类，并提出相应的译后编辑策略，为该领域译者提供参考建议，提高该领域机器翻译质量。