Efficient English Text Comprehension and Pattern Understanding Model with ChatGPT

In the context of intelligent information system models, in order to further promote the development of natural language processing and explore its application scenarios in different fields, this study studied the English text understanding and pattern recognition model based on ChatGPT middleware from the modeling and analysis level. The model proposed in this study integrates the information of text and audio input as the modeling of multi-modal semantic understanding to improve the accuracy and universality of machine language understanding in complex environments. In order to transform human natural language into structured data and hierarchy that can be understood by machines, this study first explored the significance of natural language processing and the processing challenges in real applications. Subsequently, this study gave an in-depth explanation of the design and methods of the model, focusing on the key role of improving ChatGPT middleware in simplifying the modal fusion process and thus ensuring the accuracy and applicability of the output results. In the experimental verification part, this study used a variety of self-collected data sets to evaluate the model. The experimental results show that the proposed model has an accuracy of 97.5% for text semantic understanding, an accuracy of 98.2% for pattern recognition tasks, and an overall accuracy of 99.2%. Compared with traditional algorithms, proposed model has better comprehensive performance.