Background　This　study　aims　to　propose　a　breast　cancer　prediction　model　for　early　diagnosis　and　prognosis　management　of　breast　cancer.　Objective　In　order　to　explore　the　pathogenesis　of　breast　cancer　and　develop　accurate　breast　cancer　screening　and　treatment　methods,　we　have　used　machine-learning　technologies　to　conduct　an　in-depth　study　of　breast　cancer　genetic　data　to　obtain　new　breast　cancer　signature　and　prognostic　prediction　models.　Methods　We　explored　an　optimal　cluster　by　unsupervised　clustering　methods　with　different　expression　genes　(DEGs)　between　normal　(n　=　113)　and　tumour　(n　=　1,102)　samples.　Using　least　absolute　shrinkage　and　selection　operator　(LASSO)　regression,　we　selected　four　biomarkers　to　develop　a　predictive　model　by　Cox　regression　method　in　the　training　set　(n　=　1,083)　and　validated　its　predictive　accuracy　and　independence　in　the　testing　sets　(n　=　2,480).　Then　Gene　Set　Enrichment　Analysis　(GSEA)　revealed　enriched　biological　pathways　in　clusters.　Finally,　we　constructed　a　nomogram　including　this　signature　and　other　significant　risk　factors　to　predict　survival　rates　in　patients.　Results　Four　mRNAs　(CD163L1,　QPRT,　NKAIN1　and　TP53AIP1)　between　two　clusters　from　4,938　DEGs　were　identified,　and　then　a　four-gene　model　(risk　scores　=　0.454*CD163L1-0.360*NKAIN1　+　0.581*QPRT　+　0.788*TP53AIP1)　was　established　to　divide　patients　into　high-　and　low-risk　group　with　significantly　different　prognosis　(p　＜　0.0001)　in　the　training　set.　Integrated　analysis　revealed　dysregulated　molecular　processes　including　predominantly　oncogenic　signalling　pathway,　cell　cycle　and　DNA　repair　in　high-risk　group　but　enriched　metabolism　pathway　in　low-risk　group.　In　addition,　this　model　had　similar　predictive　value　(HR　＞1.60;　p　＜　0.05)　in　three　independent　validation　sets,　which　could　predict　survival　independently　with　more　power　compared　with　single　clinical　factor.　In　addition,　the　nomogram　could　predict　the　prognosis　of　breast　cancer　patients　precisely　in　the　training　set　and　another　three　testing　sets.　Conclusion　This　model　could　predict　prognosis　of　breast　cancer　patients　precisely　and　independently,　and　provide　evidence　to　make　treatment　decisions　and　design　clinical　trials.