【Abstract】 Background Myocardial ischemia reperfusion (MIR) may not only exacerbate the underlying cardiovascular illness such as leading to heart failure aggravating the original cardiovascular disease, but it may also result in distant organ dysfunction, considerably increasing patient morbidity and death. Thus, it is critical to research the molecular regulatory network in MIR to get a better understanding of the processes underlying MIR for therapeutic application. Methods Expression data of MIR and sham samples of mice in GSE148864 were downloaded from gene expression omnibus (GEO) database. DEmiRNAs between MIR and sham samples were identified by limma R package. The biological function of DEmiRNAs were analyzed by DIANA-miRPath tool. The diagnostic value of DEmiRNAs were evaluated by receiver operation curves (ROC), and DEmiRNAs with the areas under curves (AUC) greater than 0.85 were identified as key miRNAs in MIR. The miRWalk software was applied to predict the mRNAs regulated by key miRNAs. The biological function of predicted mRNAs were analyzed by clusterProfiler R package. TRRUST database was used to extract the transcription factors (TFs) from predicted mRNAs and to identify corresponding target genes. STING database was used to predict the interactions of predicted mRNAs. A miRNA-TF-gene regulatory network was constructed by extracting miRNA-mRNA, mRNA-mRNA, miRNA-miRNA, miRNA-TF and TF-mRNA relation pairs. Moreover, there- and four-node regulatory motifs were detected by FANMOD software to further identify the most significant regulatory motif in MIR. Results 2 Between injury and sham samples in the GSE148864 dataset, a total of 12 DEmiRNAs were discovered, most of which were involved in mitochondrial function, lipid metabolism, and energy metabolism-related KEGG pathways. The mmu-miR-1843a-5p, mmu-miR-5621-3p, mmu-miR-5113, mmu-miR-8116, mmu-miR-144-5p, mmu-miR-3962, mmu-miR-103-3p, mmu-miR-505-5p and mmu-miR-299b-3p were identified as key miRNAs in MIR injury with AUC greater than 0.85. Based on miRWalk, STRING and TRRUST database, a miRNA-TF-gene network composed of 1,466 nodes and 2,121 edges was constructed and visualized by Cytoscape software. Furthermore, using FANMOD tool, seven three-node and 15 four-node regulatory motifs were identified, and ID: 2182 with the highest Z score was selected as the most significant motif in MIR injury. Conclusion Our research identified nine critical miRNAs involved in MIR injury and established a regulatory network between miRNAs, TFs, and genes. These discoveries might serve as a molecular foundation for future research into MIR damage.