Lightweight English Text Classification with Deep Learning Based on Complex System Theory

Globalization and the development of information technology have driven a surge in English text data, and there is an urgent need for efficient classification. To improve the generalization ability of English text classification in small-sample and cross-domain scenarios and to achieve lightweight models, this study combines complex systems theory with deep learning to construct a graph neural network model that fully accounts for the distributional characteristics of text samples. A two-level meta-distillation method, combined with meta-learning strategies, dynamically adjusts the teacher model's parameters and optimizes the entire knowledge-transfer process. The experimental results show that the classification performance of the proposed model in few-shot and cross-domain text classification tasks is significantly superior to that of traditional graph neural networks and other mainstream comparative models. In terms of lightweighting, the SM generated by the two-stage meta-distillation mechanism maintains an extremely high level of performance retention on multi-topic text classification datasets, while significantly reducing computational time. In addition, this method can maintain high efficiency and stable classification performance in long text processing scenarios and offers clear advantages in computational efficiency compared with traditional distillation methods and other methods reported in the literature. The proposed method effectively enhances the classification performance of English text in low-sample and cross-domain application scenarios while significantly reducing the computational cost, thus providing a feasible and efficient technical solution for English text classification in resource-constrained environments.