The　rapid　development　of　large　language　models　has　significantly　reduced　the　cost　of　producing　rumors,　which　brings　a　tremendous　challenge　to　the　authenticity　of　content　on　social　media.　Therefore,　it　has　become　crucially　important　to　identify　and　detect　rumors.　Existing　deep　learning　methods　usually　require　a　large　amount　of　labeled　data,　which　leads　to　poor　robustness　in　dealing　with　different　types　of　rumor　events.　In　addition,　they　neglect　to　fully　utilize　the　structural　information　of　rumors,　resulting　in　a　need　to　improve　their　identification　and　detection　performance.　In　this　article,　we　propose　a　new　rumor　detection　framework　based　on　bi-directional　multi-level　graph　contrastive　learning,　BiMGCL,　which　models　each　rumor　propagation　structure　as　bidirectional　graphs　and　performs　self-supervised　contrastive　learning　based　on　node-level　and　graph-level　instances.　In　particular,　BiMGCL　models　the　structure　of　each　rumor　event　with　fine-grained　bidirectional　graphs　that　effectively　consider　the　bi-directional　structural　characteristics　of　rumor　propagation　and　dispersion.　Moreover,　BiMGCL　designs　three　types　of　interpretable　bi-directional　graph　data　augmentation　strategies　and　adopts　both　node-level　and　graph-level　contrastive　learning　to　capture　the　propagation　characteristics　of　rumor　events.　Experimental　results　on　real　datasets　demonstrate　that　our　proposed　BiMGCL　achieves　superior　detection　performance　compared　against　the　state-of-the-art　rumor　detection　methods.