Research and Application of Artificial Intelligence Large Language Models Based on Feature Enhancement

In recent years, natural language processing has become a crucial research direction in artificial intelligence. The Transformer model, in particular, has emerged as a foundational model in the field of natural language processing. Transformer and its various variants have significantly improved the performance of many natural language processing tasks. Therefore, this study focuses on the research and application of artificial intelligence large language models based on feature enhancement. Firstly, the paper proposes a pre-trained language model based on the Transformer architecture. Secondly, it analyzes BERT model's pre-training on representing news content to enhance the model's accuracy. Finally, through experimentation, this study demonstrates that the proposed model and algorithm effectively optimize language vocabulary in news, thereby improving its performance in tasks.