To analyze the directions for future research suggested and to project future
research plans, we extract relevant text from these publications with respect to
COVID-19-related research based on 54,136 relevant academic journals published
from the initial outbreak of COVID-19 in January 2020 until December 2020.
First, we extract and preprocess the corpus and then determine that, according
to the Elbow method, the optimal number of clusters is 7. Then, we construct a
text clustering model based on an autoencoder, with the support of an artificial
neural network. Distance measurements, such as correlation, cosine, Braycurtis,
and Jaccard are compared, and the clustering results are evaluated with normal
mutual information. The results show that cosine similarity has the best effect
on clustering of COVID-19-related documents. A topic model analysis shows that
the directions of future research can mainly be grouped into the following seven
categories: infectivity testing, genome analysis, vaccine testing, diagnosis and
infection characteristics, pandemic management, nursing care, and clinical
testing. Among them, the topics of pandemic management, diagnosis and infection
characteristics, and clinical testing trended upward in proportion to future
directions. The topic of vaccine testing remains steady over the observation
window, whereas other topics (infectivity testing, genome analysis, and nursing
care) slowly trended downward. Among all the topics, medical research comprises
80%, and about 20% of the topics are related to public management, government
functions, and economic development. This study enriches our scientific
understanding of COVID-19 and helps us to effectively predict future scientific
research output on COVID-19.