With　the　rapid　development　of　the　internet　and　social　media,　extracting　emergency　events　from　online　news　reports　has　become　an　urgent　need　for　public　safety.　However,　current　studies　on　the　text　mining　of　emergency　information　mainly　focus　on　text　classification　and　event　recognition,　only　obtaining　a　general　and　conceptual　cognition　about　an　emergency　event,　which　cannot　effectively　support　emergency　risk　warning,　etc.　Existing　event　extraction　methods　of　other　professional　fields　often　depend　on　a　domain-specific,　well-designed　syntactic　dependency　or　external　knowledge　base,　which　can　offer　high　accuracy　in　their　professional　fields,　but　their　generalization　ability　is　not　good,　and　they　are　difficult　to　directly　apply　to　the　field　of　emergency.　To　address　these　problems,　an　end-to-end　Chinese　emergency　event　extraction　model,　called　EmergEventMine,　is　proposed　using　a　deep　adversarial　network.　Considering　the　characteristics　of　Chinese　emergency　texts,　including　small-scale　labelled　corpora,　relatively　clearer　syntactic　structures,　and　concentrated　argument　distribution,　this　paper　simplifies　the　event　extraction　with　four　subtasks　as　a　two-stage　task　based　on　the　goals　of　subtasks,　and　then　develops　a　lightweight　heterogeneous　joint　model　based　on　deep　neural　networks　for　realizing　end-to-end　and　few-shot　Chinese　emergency　event　extraction.　Moreover,　adversarial　training　is　introduced　into　the　joint　model　to　alleviate　the　overfitting　of　the　model　on　the　small-scale　labelled　corpora.　Experiments　on　the　Chinese　emergency　corpus　fully　prove　the　effectiveness　of　the　proposed　model.　Moreover,　this　model　significantly　outperforms　other　existing　state-of-the-art　event　extraction　models.