To address the challenges posed by complex regulations and policies in compliance management, Natural Language Processing (NLP) algorithms are employed to enhance the efficiency and accuracy of text analysis. Utilizing BERT and Transformer as core models, these systems automatically generate compliance documents, categorize compliance-related texts, and dynamically monitor user behavior, significantly improving compliance detection and risk assessment. Data shows that text classification based on BERT achieved an accuracy of 93.4%, and the efficiency of automatic compliance document generation is 150 times higher compared to manual writing, with user satisfaction for automated compliance review maintaining above 85%. Analysis suggests that the application of NLP technology in compliance management can significantly reduce compliance costs and enhance risk alert capabilities.