Objective: To predict the mechanism of radix paeoniae rubra in the prevention and treatment of lung cancer through network pharmacology. Methods: The chemical components of radix paeoniae rubra were retrieved from the TCMSP database, and its action targets were predicted using the SwissTarget Prediction database. The targets related to lung cancer were collected from the OMIM database and GeneCards database. The intersection of the targets of radix paeoniae rubra components and the targets related to lung cancer was screened.The related target proteins were collected using the String platform, and Gene Ontology annotation analysis was conducted with the DAVID database. Visualization analysis was performed using Cytoscape 3.9.1 software. The protein-protein interaction network was constructed using the Centiscape 2.2 plugin software to obtain the core targets. Through PPI, GO and KEGG analysis, 8 targets with high correlation between radix paeoniae rubra and lung cancer were obtained. The 3D structures of paeoniflorin, ellagic acid and baicalein were obtained from the PubChem database and saved as Sdf files. Molecular docking was performed between some of the targets and paeoniflorin, ellagic acid and baicalein. Target information was found in the Uniport database and PDB database. Molecular docking was conducted in AutodockVina and Openbabel to obtain the docking energy. Molecular docking visualization was performed using PyMol software. Finally, the mechanism of radix paeoniae rubra in the prevention and treatment of lung cancer was further analyzed. Based on the initial screening of 8 key genes, the significantly associated extended pathways were selected using KEGG, and molecular docking was conducted on all targets in these pathways. The docking effect was judged by the standard of binding energy ≤ -5.0 kcal·mol-1. Results: Thirteen compounds and 563 action targets were screened from the traditional Chinese medicine radix paeoniae rubra. The intersection with 2 376 lung cancer-related targets yielded 223 common targets. The protein-protein interaction network had 222 nodes and 4 678 edges. After screening, 39 nodes and 625 edges were obtained, and there were 39 core targets. GO annotation yielded 887 biological processes, 110 cellular components, and 197 molecular functions. The eight targets with high correlation between radix paeoniae rubra and lung cancer were ESR1, EGFR, KRAS2, KRAS, ERBB1, HRAS, MEK1, and MAP2K1, all of which showed good docking activity with the core active components of radix paeoniae rubra. Based on the initial screening of 8 target sites, the RAS-ERBB- MAPK signaling axis-related pathways were identified as the main signaling pathways through KEGG screening. After screening and determining the RAS-ERK, ERBB, MAPK, and Ras signaling pathways, molecular docking confirmed that the core active components of radix paeoniae rubra had good binding. Conclusion: Network pharmacology intuitively demonstrates that radix paeoniae rubra may exert therapeutic effects on the proliferation of lung cancer cells through the multi-target and multi-pathway synergistic inhibition of the RAS-ERBB-MAPK signaling axis, including the RAS-ERK, ERBB, MAPK, and Ras signaling pathways, providing a theoretical basis for the subsequent development of drugs for the treatment of lung cancer.