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Although various endogenous and exogenous IFN-I therapeutic strategies have been developed, reports of immune cell dysfunction resulting from IFN-I signal enhancement indicate that optimal strategies have yet to be established. However, heterogeneous data of IFN-I are currently spread across multiple public databases and lack systematic integration, which poses challenges for knowledge acquisition and clinical research promotion. Herein, we develop the IFN-I Knowledge Base (IFNIKB), the first specific database for IFN-I. It integrates 26\u2009273 literature articles on IFN-I antitumour immunity, 2202 clinical trial records, and data on 8372 genes and 7164 proteins across 654 species. Furthermore, we design an automated workflow for knowledge discovery from literature. Users can construct on-demand knowledge graphs to explore entities and relationships through interactive visualizations, temporal trends, and network topology analyses. Additionally, we provide tools for multiple sequence alignment, sequence identity computation, and phylogenetic analysis to interpret IFN-I from molecular perspectives. Therefore, researchers can employ IFNIKB to conveniently acquire knowledge, propose hypothesis, optimize experimental design, and identify potential clinical therapeutic targets. 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