Entity　alignment　refers　to　discovering　the　entity　pairs　with　the　same　realistic　meaning　in　different　knowledge　graphs.　This　technology　is　of　great　significance　for　completing　and　fusing　knowledge　graphs.　Recently,　methods　based　on　knowledge　representation　learning　have　achieved　remarkable　achievements　in　entity　alignment.　However,　most　existing　approaches　do　not　mine　hidden　information　in　the　knowledge　graph　as　much　as　possible.　This　paper　suggests　SCMEA,　a　novel　cross　-lingual　entity　alignment　framework　based　on　multiaspect　information　fusion　and　bidirectional　contrastive　learning.　SCMEA　initially　adopts　diverse　representation　learning　models　to　embed　multi　-aspect　information　of　entities　and　integrates　them　into　a　unified　embedding　space　with　an　adaptive　weighted　mechanism　to　overcome　the　missing　information　and　the　problem　of　differentaspect　information　are　not　uniform.　Then,　we　propose　a　stacked　relation　-entity　co　-enhanced　model　to　further　improve　the　representations　of　entities,　wherein　relation　representation　is　modeled　using　an　Entity　Collector　with　Global　Entity　Attention.　Finally,　a　combined　loss　function　based　on　improved　bidirectional　contrastive　learning　is　introduced　to　optimize　model　parameters　and　entity　representation,　effectively　mitigating　the　hubness　problem　and　accelerating　model　convergence.　We　conduct　extensive　experiments　to　evaluate　the　alignment　performance　of　SCMEA.　The　overall　experimental　results,　ablation　studies,　and　analysis　performed　on　five　cross　-lingual　datasets　demonstrate　that　our　model　achieves　varying　degrees　of　performance　improvement　and　verifies　the　effectiveness　and　robustness　of　the　model.