Abstract:

Objective: The neural network, a model based on statistics, is widely used to predict the popularity of microblog and WeChat official account. Based on that, this study aimed to explore the value of neural network in predicting the popularity of academic papers, and to provide a new auxiliary detection method for evaluating the level of academic paper. Methods: The titles, abstracts, keywords and publication days of papers from the domestic core journals of “Respiratory disease and tuberculosis” (Chinese Journal of Antituberculosis, Chinese Journal of Tuberculosis and Respiratory Diseases, Journal of Clinical Pulmonary Medicine, Chinese Journal of Respiratory and Critical Care Medicine, International Journal of Respiration, and Chinese Journal of Lung Diseases (Electronic Edition)) were collected as the input layer of the neural network from 2019 to 2021, and the features was obtained using segmentation tools to segment the input layer, thereby the citation count of the papers was predicted. Results: From 2019 to 2021, a total of 4729 papers were published in the core journals of “Respiratory disease and tuberculosis”, and 1690, 1534 and 1505 papers were published in each year from 2019 to 2021. According to the classification of citation frequency, from 2019 to 2021, the number of highly cited papers (30 to 250 times), moderately cited papers (4 to 29 times), low cited papers (1 to 3 times), and zero cited papers in the core journals of “Respiratory disease and tuberculosis” were 46, 1362, 1872, and 1449, respectively, accounting for 0.97%, 28.80%, 39.59%, and 30.64%. Using neural networks, the accuracy, precision, and recall of predicting article citation rates had reached 99.68%, 99.63%, and 99.65%, respectively. Conclusion: As a method of artificial intelligence technology, neural networks could be gradually introduced into the academic field to provide an auxiliary detection method for objectively and fairly evaluating the level of manuscripts, in order to compensate for the limitations of initial and external review by editors.

Key words: Neural networks (computer), Journal article, Forecasting