The Application of Deep Learning Method Combined with Attention Mechanism in Text Classification

In this paper, we explore the application of a deep learning approach integrated with an attention mechanism for text classification, aiming to enhance the performance and robustness of complex text classification tasks. Addressing the limitations of traditional text classification methods in capturing contextual dependencies, handling long texts, and dealing with semantic ambiguity, we propose an innovative model that leverages deep learning architectures combined with a self-attention mechanism to improve feature extraction by dynamically focusing on key information within the text. The model employs word embeddings and a hierarchical network structure, with a multi-head attention mechanism at its core. Additionally, the model is optimized for specific task objectives to achieve efficient multi-class text classification. Experimental results on multiple publicly available datasets show that the proposed method achieves superior performance compared to existing state-of-the-art approaches, particularly in terms of accuracy and F1 score. This is especially evident when handling long and complex text data, where the model demonstrates enhanced capability in capturing relevant features and improving classification accuracy. Furthermore, a visual analysis of the attention weight distribution is conducted, offering insights into the model's text feature extraction mechanism. These findings provide valuable theoretical contributions and practical guidance for advancing text classification techniques in the field of natural language processing.