[目的/意义]构建一套融合多维特征指标与深度学习方法的潜在高影响力论文自动预测框架，提升对论文学术价值与影响力的综合刻画与精准预测能力，为科研评价优化、学术资源配置及科技决策提供方法支撑与技术路径。[方法/过程]从科技论文自身特征、内容特征和被引特征三个维度构建测度科技论文内容价值和影响力的三维特征指标体系，继而设计并实现融合论文三维特征指标和深度学习模型的潜在高影响力论文自动预测框架，并以社会科学代表性学科经济学为例，实现学科潜在高影响力论文的深度学习预测，并对潜在高影响力论文的特征表现进行计量比较。[结果/结论]科技文献不同维度指标之间的差异较大，构建的三维特征指标体系与特征向量空间，融合深度学习预测模型，能够全方位测度科技论文内容价值和影响力，同时较优的预测效果能够推动海量文献中潜在高影响力论文的自动预测与推荐应用。人工神经网络和TabNet在预测准确率和精确率上表现出色，但在召回率、P-R面积和AUC值等指标上不及传统机器学习模型，使用论文被引特征预测高影响力论文的效果表现比使用论文自身特征或内容特征更优异。潜在高影响力论文在自身特征、内容主题、引用动态等多维特征指标上呈现出显著的优势。

[Purpose/significance] This study aims to develop an automated prediction framework for identifying poten⁃tially high-impact papers by integrating multidimensional feature index with deep learning methods, enhancing the comprehensive characterization and accurate prediction of academic value and impact, thereby providing methodologi⁃cal support and technical pathways for optimizing research evaluation, allocating academic resources, and informing science and technology decision-making. [Method/process] In order to realize the accurate prediction of potentially high-impact papers in the literature of social science field, this study firstly constructs a three-dimensional feature in⁃dex system of scientific and technological papers from the three dimensions of the papers' own features, content fea⁃tures and cited features. Then, this study designs and realizes the automatic prediction framework of potentially highimpact papers, which integrates the three-dimensional feature index of papers and deep learning model, to realize the deep learning prediction of potentially high-impact papers in the massive literature from the major social sciences dis⁃cipline named economics. Finally, the differences and advantages in features of potentially high-impact papers are sys⁃tematically compared and analyzed. [Result/conclusion] There are significant differences among various dimensional index in scientific and technical literature. The constructed three-dimensional feature index system and feature vector space, combined with deep learning prediction models, can comprehensively measure the value and influence of scien⁃tific and technological papers. At the same time, the superior prediction effect can promote the automatic prediction and recommendation application of potentially high-impact papers in massive literature. Artificial neural networks and TabNet perform well in prediction accuracy and precision, but are inferior to traditional machine learning models in metrics such as recall, P-R area, and AUC value. Using citation features of papers outperforms using the paper's own features or content features to predict high impact papers. Potentially high-impact papers exhibit significant advantag⁃es in multidimensional feature index, including papers' own features, thematic features, citation dynamics, etc.