Knowledge　Graphs　(KGs)　serve　as　valuable　auxiliary　information　to　improve　the　accuracy　of　recommendation　systems.　Previous　methods　have　leveraged　the　knowledge　graph　to　enhance　item　representation　and　thus　achieve　excellent　performance.　However,　these　approaches　heavily　rely　on　high-quality　knowledge　graphs　and　learn　enhanced　representations　with　the　assistance　of　carefully　designed　triplets.　Furthermore,　the　emergence　of　knowledge　graphs　has　led　to　models　that　ignore　the　inherent　relationships　between　items　and　entities.　To　address　these　challenges,　we　propose　a　Self-Derived　Knowledge　Graph　Contrastive　Learning　framework　(CL-SDKG)　to　enhance　recommendation　systems.　Specifically,　we　employ　the　variational　graph　reconstruction　technique　to　estimate　the　Gaussian　distribution　of　user-item　nodes　corresponding　to　the　graph　neural　network　aggregation　layer.　This　process　generates　multiple　KGs,　referred　to　as　self-derived　KGs.　The　self-derived　KG　acquires　more　robust　perceptual　representations　through　the　consistency　of　the　estimated　structure.　Besides,　the　self-derived　KG　allows　models　to　focus　on　user-item　interactions　and　reduce　the　negative　impact　of　miscellaneous　dependencies　introduced　by　conventional　KGs.　Finally,　we　apply　contrastive　learning　to　the　self-derived　KG　to　further　improve　the　robustness　of　CL-SDKG　through　the　traditional　KG　contrast-enhanced　process.　We　conducted　comprehensive　experiments　on　three　public　datasets,　and　the　results　demonstrate　that　our　CL-SDKG　outperforms　state-of-the-art　baselines.　©　2024　ACM.