RETRACTED ARTICLE: Urban land ecological evaluation and English translation model optimization based on machine learning

被引:0
作者
Lin Wang
机构
[1] University of Shanghai for Science and Technology,College of Foreign Languages
关键词
Ecological evaluation; Land use; Translation model; Machine learning;
D O I
10.1007/s12517-021-07334-6
中图分类号
学科分类号
摘要
Land is the basis for human survival and development. The increase in population and urbanization will lead to an increase in the contradiction between ecological and economic interests. At present, the large-scale development of land has brought different degrees of ecological problems to each region of the country. It not only destroys the safety and stability of the land ecosystem, but also affects the sustainable development of the economy and society. In the research of land ecology, the main focus is on ecological problems, and there is insufficient research on the structure and function of land ecology. This paper uses SPSS, Fragstats, GIS software calculations, and data processing to analyze the spatial distribution of different land ecosystems under different ecosystem types and conduct evaluations to comprehensively improve the ecological quality of the land. With the development of information technology, machine translation and computer-assisted English translation technology are increasingly appearing in people's lives. Translation software and machine translation tools are used for daily foreign product translation needs and overseas travel. Based on national conditions, explore China’s development advantages in artificial intelligence and machine translation industries, and study ways to improve machine translation in the era of big data. In translation practice, English translation based on the combination of computer-aided translation and human translation has made great progress in recent years, which not only improves the speed and accuracy of translation, but also provides a more solid foundation for the long-term and healthy development of the translation industry in the future.
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