The　medical　record　system　is　becoming　more　and　more　irreplaceable　in　the　medical　industry,　and　the　electronic　medical　record　data　continues　to　grow　over　time.　There　is　a　lot　of　knowledge　and　information　in　these　accumulated　medical　resources　that　can　be　used　for　medical　services,　but　how　to　obtain　this　valuable　medical　information　is　a　difficult　problem　that　needs　to　be　overcome.　Event　extraction　belongs　to　information　extraction　technology,　which　is　an　effective　solution　that　can　automatically　mine　knowledge　and　information　from　text　data.　Many　studies　have　applied　it　to　the　text　data　of　electronic　medical　records　to　extract　medical　events　related　to　medical　treatment.　They　have　achieved　certain　results　for　medical　services.　However,　these　studies　usually　lack　the　synergistic　consideration　of　global　features　and　local　features　of　medical　text　information　in　terms　of　Chinese　medical　record　text　mining　and　utilization.　To　better　solve　this　problem,　we　try　to　propose　a　hybrid　neural　network　model　(BCBC)　based　on　CNN-BILSTM-CRF.　By　integrating　CNN　and　BILSTM,　the　local　and　global　features　of　the　text　are　comprehensively　extracted,　which　makes　up　for　the　insufficient　semantic　capture　of　a　single　model　in　the　traditional　method.　Through　experimental　verification,　the　hybrid　neural　network　model　BCBC　proposed　in　this　paper　outperforms　other　previous　advanced　methods　in　event　extraction　and　can　efficiently　complete　the　event　extraction　task.