Faced with the current large-scale public health emergency, collecting, sorting, and analyzing biomedical information related to the "SARS-CoV-2" should be done as quickly as possible to gain a global perspective, which is a basic requirement for strengthening epidemic control capacity. However, for human researchers studying viruses and hosts, the vast amount of information available cannot be processed effectively and in a timely manner, particularly if our scientific understanding is also limited, which further lowers the information processing efficiency. We present TWIRLS (Topic-wise inference engine of massive biomedical literatures), a method that can deal with various scientific problems, such as liver cancer, acute myeloid leukemia, and so forth, which can automatically acquire, organize, and classify information. Additionally, this information can be combined with independent functional data sources to build an inference system via a machine-based approach, which can provide relevant knowledge to help human researchers quickly establish subject cognition and to make more effective decisions. Using TWIRLS, we automatically analyzed more than three million words in more than 14,000 literature articles in only 4 hr. We found that an important regulatory factor angiotensin-converting enzyme 2 (ACE2) may be involved in host pathological changes on binding to the coronavirus after infection. On triggering functional changes in ACE2/AT2R, the cytokine homeostasis regulation axis becomes imbalanced via the Renin-Angiotensin System and IP-10, leading to a cytokine storm. Through a preliminary analysis of blood indices of COVID-19 patients with a history of hypertension, we found that non-ARB (Angiotensin II receptor blockers) users had more symptoms of severe illness than ARB users. This suggests ARBs could potentially be used to treat acute lung injury caused by coronavirus infection.