用NLP和机器学习自动分类推荐论文，解决科研信息过载问题。
Automated Research Article Classification and Recommendation Using NLP and ML
- 结合TF-IDF与逻辑回归，实现论文自动分类。
- 分类准确率达69%，推荐模块基于向量相似度检索相关论文。
- 适合需要高效找文献的科研人员，系统可扩展性强。
在数字时代，科学出版物的指数级增长使得研究人员难以高效识别和获取相关文献。本文提出一种基于自然语言处理（NLP）与机器学习的自动化论文分类与推荐框架。利用涵盖三十余年arXiv.org数据集，评估了多种特征提取方法（TF-IDF、Count Vectorizer、Sentence-BERT、USE、Mirror-BERT）与多种机器学习分类器（逻辑回归、SVM、朴素贝叶斯、随机森林、梯度提升树、k近邻）的组合。实验表明，使用TF-IDF的逻辑回归在分类任务中表现最佳，准确率达到69%。为补充分类功能，系统引入基于向量文章余弦相似度的推荐模块，实现高效的相关论文检索。该系统直接应对数字图书馆中的信息过载挑战，展示了可扩展的数据驱动文献发现解决方案。
原文摘要 · Abstract (English)
In the digital era, the exponential growth of scientific publications has made it increasingly difficult for researchers to efficiently identify and access relevant work. This paper presents an automated framework for research article classification and recommendation that leverages Natural Language Processing (NLP) techniques and machine learning. Using a large-scale arXiv.org dataset spanning more than three decades, we evaluate multiple feature extraction approaches (TF--IDF, Count Vectorizer, Sentence-BERT, USE, Mirror-BERT) in combination with diverse machine learning classifiers (Logistic Regression, SVM, Naïve Bayes, Random Forest, Gradient Boosted Trees, and k-Nearest Neighbour). Our experiments show that Logistic Regression with TF--IDF consistently yields the best classification performance, achieving an accuracy of 69\%. To complement classification, we incorporate a recommendation module based on the cosine similarity of vectorized articles, enabling efficient retrieval of related research papers. The proposed system directly addresses the challenge of information overload in digital libraries and demonstrates a scalable, data-driven solution to support literature discovery.
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