Research and Applications of Large Language Models for Converting Unstructured Data into Structured Data

The rapid growth of data has led to a significant increase in unstructured data, such as text, audio, and images, which dominate modern information processing. However, the complexity of unstructured data presents challenges for automated analysis and processing. Converting unstructured data into structured formats is crucial for tasks like data mining, information extraction, and knowledge graph construction. Traditional methods that rely on manual rules or statistical models struggle with complex, context-dependent data. Recently, large language models (LLMs), such as GPT-4 and BERT, have demonstrated great potential in unstructured data processing due to their powerful natural language understanding and generation capabilities. This study explores the application of large language models in transforming unstructured data into structured formats. It begins by reviewing the limitations of traditional approaches and then presents a framework for unstructured data processing using LLMs, covering data preprocessing, information extraction, and structured representation. The study's experiments demonstrate the superior performance of LLMs in handling various types of unstructured data, particularly in tasks like named entity recognition, relation extraction, and contextual understanding. The results show that LLMs achieve higher accuracy and generalization while reducing reliance on manual rules. The paper discusses the strengths and limitations of this method, proposing future improvements, such as combining domain-specific knowledge for model optimization and expanding applications to multimodal data processing. The research highlights the promising role of large language models in converting unstructured data into structured formats.