The　widely　deployed　of　hospital　information　systems　causes　an　explosive　growth　of　the　electronic　medical　records　(EMRs).　It　makes　the　medical　structured　processing　technologies　become　critical　to　find　researchable　data　in　the　large　medical　dataset.　However,　the　high　quality　structured　processing　is　a　challenging　task,　in　particular　due　to　the　inherent　complexity　and　polysemy　of　medical　terminology.　In　this　paper,　we　propose　a　novel　approach　to　achieve　the　joint　extraction　of　events　in　Chinese　electronic　medical　records,　which　solves　the　problem　of　cascading　error　transmission　in　traditional　models　and　the　ambiguity　of　Chinese　characters.　We　first　use　the　Bi-directional　Encoder　Representation　from　Transformers(BERT)　model　to　mine　features　from　the　preprocessed　medical　data;　then　based　on　the　characteristics　of　Chinese,　we　use　the　Bi-directional　Long　Short-Term　Memory(BILSTM)　model　to　capture　the　semantic　information　of　the　context.　The　experiments　were　conducted　on　a　real　dataset.　The　F1　score　of　our　model　in　the　identification　and　classification　tasks　of　event　triggers　and　arguments　is　the　highest,　reaching　71.6,　68.1,　55.4　and　46.9,　respectively,　which　proves　the　effectiveness　of　the　proposed　method.