Research on the Evaluation Model of Automatic Real-Time Data Processing for Interpreting Quality Based on Artificial Intelligence

This paper proposes an automatic real-time data processing evaluation model for interpreting quality based on artificial intelligence, aiming to achieve efficient quality monitoring through natural language processing (NLP) and machine learning technology. The system combines data processing and language correction algorithms to analyze the quality of interpreted texts in real time in multiple dimensions. Specifically, the model design includes modules such as semantic matching, syntactic structure analysis, and sentiment attitude determination, and optimizes the performance of the model in different contexts through data-driven optimization. To verify the effectiveness of the model, this paper conducted many simulation tests and evaluated the model using precise data analysis methods. In the test data set, the model performed better than traditional methods in terms of real-time and accuracy of interpreting, with an accuracy improvement of 12% and a feedback speed improvement of about 35%. The experimental results show that the model has significant advantages in stability, accuracy and adaptability, and can significantly improve the efficiency and accuracy of interpreters and meet the needs of practical applications.