Application Research on Large Language Model Attention Mechanism in Automatic Classification of Book Content

The LLaMA (Large Language Model Attention Mechanism) application in automatic book content classification is receiving increasing attention. The main content of this article revolves around traditional book classification models and improved LLaMA structures, delving into the application and advantages of LLaMA in book classification tasks. Firstly, this article introduces the task description of traditional book classification models and rule-based classification methods. Next, this article elaborates on the design of improving the LLaMA structure, including introducing attention mechanisms, replacing traditional attention terms with self-attention functions, initializing word embedding set functions, and designing the overall structure of the LLaMA model. Furthermore, this article demonstrates a book classification method based on the improved LLaMA algorithm, including the LLaMA algorithm itself, the improved LLaMA algorithm, and its specific application process in book classification. Finally, the effectiveness and accuracy of the book classification method based on LLaMA were verified in practical applications through an example analysis. This article concludes that the improved LLaMA model has significant advantages in automatic book content classification, which can improve the accuracy and efficiency of classification and provide a new technological path for the field of book classification.