Relation　extraction,　which　is　a　subtask　of　NLP　(natural　language　processing)　field,　its　target　is　to　identify　the　entities　in　texts　and　extract　the　relation　between　entities.　Previous　works　prove　that　neural　networks　are　feasible　for　relation　extraction.　CNN　(convolutional　neural　networks)　and　LSTM　(long　short-term　memory)　are　two　majority　models　used　in　relation　extraction.　Further　research　shows　that　the　combination　of　CNN　and　LSTM　has　a　better　performance.　Inspired　by　the　solution　of　LVCSR　(Large-Vocabulary-Continuous-Speech-Recognition),　another　task　in　the　NLP　field,　we　propose　adding　DNN　after　the　combination　of　CNN　and　LSTM.　This　model　achieves　a　better　effect　on　the　precision-recall　curve　than　the　previous　model.　©　2021　IEEE.