Intelligent Compliance Risk Detection in the Pharmaceutical Industry via Transformer-Driven Semantic Discrimination

Abstract

This study addresses the challenges faced by pharmaceutical enterprises in compliance risk identification, including complex multi-source heterogeneous data, deep semantic dependencies in text structures, and hidden risk cues. An intelligent semantic discrimination model based on the Transformer architecture is proposed. The model builds a multi-layer self-attention mechanism and a semantic-weighted pooling module to achieve global semantic modeling and dynamic feature aggregation across regulatory documents, audit reports, and corporate disclosure texts. The embedding and positional encoding layers serve as inputs, while the multi-head attention mechanism captures cross-sentence dependencies. Residual connections and layer normalization ensure stable semantic propagation. During feature aggregation, an adaptive weighting mechanism enhances the model’s ability to focus on key risk-related semantic segments, improving both accuracy and robustness. Experimental results show that the proposed model outperforms traditional deep learning methods across multiple evaluation metrics and achieves higher accuracy, recall, and overall stability under complex text conditions. Validation on real-world pharmaceutical corpora confirms that the model effectively identifies potential compliance risk behaviors, demonstrating strong semantic understanding and risk recognition capabilities. This study provides a scalable technical framework for intelligent compliance analysis in the pharmaceutical industry and introduces a new algorithmic approach for semantic risk modeling in regulatory technology.