An Intelligent Classification Algorithm for News Topics Incorporating Large Model Language Understanding and Sample Difficulty Awareness

Category imbalance remains a pervasive challenge in multi-label text classification tasks. With the rapid development of large-scale pre-trained language models, the task of news topic classification has received renewed scholarly interest. This study proposes an intelligent classification framework that integrates the contextual representation capabilities of BERT with a difficulty-aware learning strategy. Specifically, BERT is utilized to capture deep semantic features for enhanced topic differentiation, while the Focal Loss function is employed to dynamically assign greater weight to hard-to-classify instances during model training. This combined approach effectively alleviates the adverse effects of class imbalance and label noise on classification performance. Experimental evaluations show that the proposed method surpasses conventional baselines, yielding an improvement of approximately 5% in overall accuracy. These findings suggest a promising direction for enhancing automatic news categorization.