Aiming at the problems of too many forms and insufficient acquisition of form features in the existing form-based event relationship extraction methods, this study proposes TF-ChineseERE, a Chinese event causality extraction method that combines Roberta and Bi-FLASH-SRU. The method transforms the text into labeled forms by formulating a form-filling strategy that takes advantage of the labeled relationships in the text. The study proposes the Roberta pre-training model and the bidirectional built-in flash attention simple recurrent unit (Bi-FLASH-SRU) to obtain the subject-object event features. It then uses the table feature recurrent learning module to mine the global features and finally performs table decoding to obtain event causality triples. The experiments are validated with two public datasets in the financial domain. The results show that the F₁ values of the proposed method reach 59.2% and 62.5%, respectively, with a faster training speed of the Bi-FLASH-SRU model and less number of filled forms, which proves the effectiveness of the method.