Background Idiopathic pulmonary fibrosis (IPF) is a complex lung disease. Efferocytosis was related to IPF initiation and progression. The study aimed to mine efferocytosis-related genes (ECRGs) and establish corresponding prognostic signature in IPF. Methods Differentially expressed ECRGs (DEECRGs) were obtained by overlapping differentially expressed genes (DEGs) between IPF and normal samples and ECRGs. Univariate COX and the least absolute shrinkage and selection operator (LASSO) regression were applied to construct a risk model. The model was evaluated by Kaplan-Meier (K-M) and receiver operating characteristic (ROC) curves. Multivariate Cox model was performed, nomogram was further constructed. Moreover, gene set variation analysis (GSVA) and immune infiltration of two risk groups were explored. Last, the study evaluated the predictive power of EC-related model genes in both GSE70866 training dataset and GSE10667 validation dataset. Results A risk model was constructed with 5 ECRGs (CXCR4, ODC1, AXL, DOCK5 and MERTK). K-M analysis showed IPF patients in high risk group performed noteworthy poorer survival than those in low risk group. ROC curves indicated good performance of the risk model. GSVA illustrated that biological processes of diacyl bacterial lipopeptide and amino acid betaine biosynthetic process, and KEGG pathways of clycosaminoglycan biosynthesis chondroitin sulfate and butanoate metabolism signaling pathway were significantly different in two risk groups. Immune infiltration analysis showed that there were significant differential immune cells(Mast cells, naive B cells, actiated NK cells, M0 Macrophages, resting Dendritic cell and resting Mast cell)in two risk groups. Conclusions A risk model consisting of 5 ECRGs (CXCR4, ODC1, AXL, DOCK5 and MERTK) was successfully constructed, which could provide a new idea for the prognosis of IPF.