Predicting the citation counts of academic papers is of considerable significance to scientific evaluation. This study used a four-layer Back Propagation (BP) neural network model to predict the five-year citations of 49,834 papers in the library, information and documentation field indexed by the CSSCI database and published from 2000 to 2013. We extracted six paper features, two journal features, nine author features, eight reference features, and five early citation features to make the prediction. The empirical experiments showed that the performance of the BP neural network is significantly better than those of the six baseline models. In terms of the prediction effect, the accuracy of the model at predicting infrequently cited papers was higher than that for frequently cited ones. We determined that five essential features have significant effects on the prediction performance of the model, i.e., ‘citations in the first two years’, ‘first-cited age’, ‘paper length’, ‘month of publication’, and ‘self-citations of journals’, and the other features contribute only slightly to the prediction.

预测学术论文的被引次数对科学评价具有重要意义。这项研究使用四层反向传播（BP）神经网络模型预测了CSSCI数据库索引并于2000年至2013年出版的图书馆，信息和文献领域对49,834篇论文的五年引用率。我们提取了六个论文特征，两个期刊功能，九个作者功能，八个参考功能和五个早期引用功能来进行预测。经验实验表明，BP神经网络的性能明显优于六个基线模型。在预测效果方面，该模型对不经常被引用的论文进行预测的准确性高于经常被引用的论文。