In order to enhance the accuracy of event extraction for aquatic animal disease prevention and control， and effectively address issues such as ambiguous boundaries of proprietary terms and excessively lengthy event entities during the extraction process， the research introduces the idea of dynamic weight into the event extraction method of multi-model integration. Two pre-training models，ERNIE（enhanced representation through knowledge integration）and MacBERT（MLM as correction BERT）， are used to learn the text semantic information.A gate module with dynamic weights is used to fuse features to enhance the semantic information of the original text.Pass the learned semantic information into BiLSTM （bi-directional long shortterm memory）， and constrain the output label sequence through CRF （conditional random field）.Select the ERNIE⊕MacBERT-CRF model and the ERNIE⊕MacBERT-BiLSTM-CRF model （⊕ represents the fusion method of simple addition and averaging） as the control model to conduct a comparative test of the fusion performance of the proposed method.The results show that the F1-score of this method reaches 74.15%， which is 20.02 percentage points higher than the classic model BiLSTM-CRF.The results show that this method has a better effect in the extraction of aquatic animal disease prevention and control events.