Fusion of Local and Global Context in Large Language Models for Text Classification

This study addresses the problem of insufficient context capture in text classification and proposes a large language model method enhanced with contextual mechanisms. At the input layer, raw text is transformed into vector sequences that incorporate both semantic and sequential features through the combination of embedding representation and positional encoding. A context encoder based on self-attention is then introduced to capture global dependencies within the sequence. At the same time, a context gating unit is designed to achieve dynamic fusion of local and global information, which preserves fine-grained features while strengthening overall contextual consistency. Furthermore, a global context aggregation module integrates semantic information across sentences or paragraphs, enhancing the model's ability to represent long texts and implicit semantics. In the output stage, sentence-level pooling is used to generate a unified representation, followed by a classification head to complete label prediction. To validate the effectiveness of the method, comparative experiments were conducted on a public news text classification dataset. The results show that the proposed method outperforms traditional deep learning models and mainstream large-model baselines in terms of accuracy, precision, recall, and F1-score. It maintains a more stable classification performance when dealing with semantic ambiguity and topic shifts. In addition, sensitivity experiments on hidden dimension settings demonstrate that moderate model capacity significantly improves performance, while excessive complexity may introduce redundant representations and slight overfitting. This study demonstrates the practical value of context enhancement mechanisms in large language models and provides a more robust and effective solution for text classification tasks.