Error correction in French language learning has long been a significant challenge in language education. To address this issue, our research team developed an intelligent correction system based on deep learning. This system integrates natural language processing and machine learning technologies, trained on a large-scale corpus, to achieve high-precision error identification and personalized correction. Experimental data shows that the system users’ average score improved by 32.5 points, with an error identification accuracy rate of 95.8%. A user survey revealed that 92.5% of students believed the system significantly improved their learning efficiency. This innovative achievement provides empirical support for the application of language learning technology and has important implications for improving the quality of French teaching.