In view of a solution of semantic deviation brought about by overfilling short sentences with fixed text length in event extraction, a sequence enhancement based event subject extraction method has thus been proposed. Specifically, an initial mapping of the fixed-length text is given to a dense vector through a pre-trained model. Subsequently, the dense vector corresponding to the text is bitwise multiplied by the custom Mask layer and SpatialDropout layer, thus obtaining the encoded output. Finally, the output is connected with BiGRU and Mask layers to get the decoded output, which is then mapped to an MLP layer to obtain the final result. This model can not only avoid the problem of overfitting the text representation in the pre-trained model, but also limit the semantic overexpression of the filled text. By using the financial field event subjects provided by CCKS 2022 as a dataset for different model reading comparative experiments, the experimental data obtained shows that the enhanced sequence with negative impact on filled text significantly improves the accuracy and F1 value of event subject recognition compared to traditional sequences.