Research on the Model of Natural Language Processing Technology in Biopharmaceutical Literature Mining

This paper focuses on the application of natural language processing (NLP) technology in biopharmaceutical literature mining, and innovatively proposes an original algorithm that combines the knowledge graph and attention mechanism in the biopharmaceutical field. By constructing the NLP model of the algorithm, a large number of biopharmaceutical literature mining experiments were carried out. The experimental data set comes from the authoritative biopharmaceutical literature library and is strictly annotated and preprocessed. In the experimental setting, a variety of traditional and advanced NLP models were selected for comparison. The results show that this model performs excellently in terms of accuracy, reaching 90%, which is 15% higher than the comparison Tokenization and 10% higher than the comparison Semantic Analysis; the recall rate reaches 85%, which exceeds the comparison Tokenization and B by 12% and 8% respectively. The F1 value also has a significant advantage. The study shows that the proposed algorithm can effectively improve the efficiency and accuracy of biopharmaceutical literature mining, and provide strong support for knowledge discovery and research in this field.