Design of Intelligent Translation Error Correction System Based on MS-CNN Algorithm

With the rapid development of computer and artificial intelligence technology, intelligent translation is increasingly used in daily life, but existing translation programmes are often inadequate in slang and rhetoric processing. In this paper, we innovatively use the improved MS-CNN algorithm to design a set of intelligent translation error detection system, which optimises the syntactic analysis and recognition mechanism especially for the complex scenario of translating from Chinese to English. Experimental results show that the system significantly improves the machine translation accuracy to 99.87%, demonstrating excellent performance in syntactic error correction. The study achieves functional segmentation through modular design and in-depth analysis of error types and system generalisation ability based on empirical results, which provides a clearer and more efficient model framework for translation error detection and correction, and strongly promotes the development of intelligent translation technology.