标题： 基于自然语言处理和机器学习的自动化研究文章分类与推荐 显示英文标题

标题： Automated Research Article Classification and Recommendation Using NLP and ML

摘要： 在数字时代，科学出版物的指数增长使得研究人员越来越难以高效地识别和获取相关工作。 本文提出了一种自动化框架，用于研究文章分类和推荐，该框架利用了自然语言处理（NLP）技术和机器学习。 使用一个跨越三十多年的大型arXiv.org数据集，我们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、梯度提升树和k-最近邻）的组合。 我们的实验表明，逻辑回归与TF--IDF相结合始终能获得最佳的分类性能，准确率达到69%。 为了补充分类，我们结合了基于向量化文章余弦相似性的推荐模块，从而实现了相关研究论文的高效检索。 所提出的系统直接解决了数字图书馆中的信息过载问题，并展示了一个可扩展的数据驱动解决方案，以支持文献发现。 显示英文摘要

摘要： 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, Na\"ive 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.