In today’s accelerating process of global integration, information exchange between various language groups is becoming increasingly frequent, leading to the widespread use of computer translation software. However, due to the fact that research in this field has only just begun and many aspects are not yet fully developed, it has not yet been widely used. This article intends to use clustering analysis algorithms to extract features from semantic texts in large-scale machine translation, and output feature clustering and screening results based on intelligent translation. The translation module of the intelligent translation system in this article mainly includes two parts: online translation module and local translation module, which are used for relevant data processing. On this basis, this article selects a specific number of texts to be translated and divides them into two categories based on the size of the data: large and small. In smaller source language data files, when the source language data file is 1 MB, the translation time of this article system is 22 s, and the translation efficiency of this article system is higher.