Testing dataset distribution.

The rapid proliferation of online news demands robust automated classification systems to enhance information organization and personalized recommendation. Although traditional methods like TF-IDF with Naive Bayes provide foundational solutions, their limitations in capturing semantic nuances and handling real-time demands hinder practical applications. This study proposes a hybrid news classification framework that integrates classical machine learning with modern advances in NLP to address these challenges. Our methodology introduces three key innovations: (1) Domain-Specific Feature Engineering, combining tailored n-grams and entity-aware TF-IDF weighting to amplify discriminative terms; (2) BERT-Guided Feature Selection, leveraging distilled BERT to identify contextually important words and resolve rare-term ambiguities; and (3) Computationally Efficient Deployment, achieving 95.2% of the accuracy of BERT at 1/52.4th of the inference cost. Evaluated on a balanced corpus of Sina News articles in 11 categories, the system demonstrates a test precision of 95.12% (vs. 84.43% for SVM+TF-IDF baseline), with statistically significant improvements confirmed by 5-fold cross-validation(p

在线新闻的快速普及与激增亟需鲁棒的自动化分类系统，以优化信息组织与个性化推荐服务。尽管诸如词频-逆文档频率（TF-IDF）结合朴素贝叶斯（Naive Bayes）的传统方法可提供基础解决方案，但它们在捕捉语义细微差别、适配实时需求方面存在局限，阻碍了实际应用落地。本研究提出一种融合经典机器学习与自然语言处理（Natural Language Processing, NLP）前沿进展的混合新闻分类框架，以应对上述挑战。本方法包含三项核心创新：(1) 领域专属特征工程：结合定制化n元语法（n-gram）与实体感知TF-IDF加权策略，强化判别性词项的表达能力；(2) BERT引导式特征选择：借助蒸馏BERT（distilled BERT）识别上下文关键词汇，解决稀有词歧义问题；(3) 低计算量部署方案：仅需BERT推理成本的1/52.4，即可达到BERT 95.2%的准确率。本研究在涵盖11个类别的新浪新闻（Sina News）平衡语料库上开展评测，该系统的测试精确率达95.12%（相较支持向量机（Support Vector Machine, SVM）+TF-IDF基准模型的84.43%），经五折交叉验证（5-fold cross-validation）证实，其性能提升具有统计学显著性（原文未完整给出统计量p值）