This study provides a comprehensive analysis of artificial intelligence (AI) contribution to translation industry (ACTI) research, synthesizing it over forty-one years from 1980-2024. 13220 articles were retrieved from three sources, namely WoS, Scopus, and Lens. We provided two types of analysis, viz., scientometric and thematic, focusing on cluster, subject categories, keywords, burstness, centrality and research centers as for the former. For the latter, we thematically review 18 articles, selected purposefully from the articles involved, centering on purpose, approach, findings, and contribution to ACTI future directions. The findings reveal that in the past AI contribution to translation industry was not rigorous, resulting in rule-based machine translation and statistical machine translation whose output was not satisfactory. However, the more AI develops, the more machine translation develops, incorporating Neural Networking Algorithms and (Deep) Language Learning Models like ChatGPT whose translation output has developed considerably. However, much rigorous research is still needed to overcome several problems encountering translation industry, specifically concerning low-source languages, multi-dialectical and free word order languages, and cultural and religious registers.

翻译：本研究对人工智能在翻译行业贡献（ACTI）的研究进行了全面分析，综合了从1980年至2024年共四十一年间的文献。我们从三个来源（即WoS、Scopus和Lens）检索了13220篇文章。我们提供了两种类型的分析，即科学计量分析和主题分析。前者侧重于聚类、学科类别、关键词、突发性、中心性和研究中心；后者则对从相关文章中特意选取的18篇文章进行了主题性评述，围绕其研究目的、方法、发现以及对ACTI未来方向的贡献展开。研究结果表明，过去人工智能对翻译行业的贡献并不严谨，导致了基于规则的机器翻译和统计机器翻译，其输出效果不尽如人意。然而，随着人工智能的发展，机器翻译也在不断进步，融合了神经网络算法和诸如ChatGPT之类的（深度）语言学习模型，其翻译输出质量已得到显著提升。尽管如此，仍需要更多严谨的研究来克服翻译行业面临的若干问题，特别是涉及低资源语言、多方言及自由语序语言，以及文化和宗教语域等方面的问题。