Design of an Automatic Scoring System for Text Translation Information under XML Structure

In recent years, technologies such as text segmentation, data mining, and machine learning have become the main research directions in the field of document translation in modern society. Especially, text analysis has become an important development trend in the field of information retrieval. Based on thorough research and summary, this study adopted XML (Extensible Markup Language) keyword extraction, feature selection, and classification algorithms to design and implement a simple, efficient, low-cost, efficient, and executable recommendation system framework. This method can significantly improve the understanding ability of classical Chinese, thereby improving the quality of the article. The system also adopted a modular structure, making the data extraction process simpler and faster. Afterwards, this article conducted a performance test on the automatic scoring system for translation information. The test results showed that the accuracy of word translation was stable at 100%, while the accuracy of other translations was above 85%; the system still performed stably in translating words, but there were still shortcomings in translation consistency in long paragraphs, with a minimum consistency rate of 80%; translation fluency could reach up to 100% and a minimum of 72%. The system aims to automatically rate the translation of XML structured text, providing an effective way to evaluate and compare the quality of different translation results.