Relation　extraction　is　a　task　of　automatically　detecting　and　identifying　predefined　semantic　relationships　between　identified　entities　in　text.　As　a　core　and　basic　technology　of　knowledge　acquisition　in　knowledge　engineering,　relation　extraction　endows　artificial　intelligence　with　strong　ability　of　knowledge　understanding.　Massive　text　data,　as　the　carrier　of　human　knowledge,　is　rapidly　submerged　in　the　tide　of　information　with　the　explosive　growth　of　information.　Mining　knowledge　hidden　in　these　texts,　is　not　only　the　theoretical　demand　of　natural　language　processing　but　also　the　practical　demand　of　human　civilization　inheritance.　Natural　language　processing　based　on　deep　learning　methods　has　made　great　progress　in　relation　extraction　field,　effectively　promoting　knowledge　discovering　in　texts　of　various　granularity.　However,　some　problems　in　relation　extraction　still　need　solving　in　the　practical　research　process.　In　view　of　the　existing　work,　most　of　the　relations　extraction　task　is　divided　into　two　independent　sub-tasks,　named　entity　recognition　and　relation　classification,　which　lack　the　interaction　between　named　entity　recognition　and　relation　classification　in　the　sentence,　and　cannot　handle　the　overlapping　entity　and　relationship　triples　well.　To　solve　these　problems,　a　joint　entity　and　relation　extraction　model　based　on　Encoder-Decoder　structure　is　proposed.　©　2023　IEEE.