The existing event detection models rely heavily on human-annotated data， and supervised deep learning models for event detection task often suffer from over-fitting when there is only limited labeled data. Methods of replacing time-consuming human annotation data with auto-labeled data typically rely on sophisticated pre-defined rules. To address these issues， a BERT （Bidirectional Encoder Representations from Transformers） based Mix-text ADversarial training （BMAD） method for Chinese event detection was proposed. In the proposed method， a weakly supervised learning scene was set on the basis of data augmentation and adversarial learning， and a span extraction model was used to solve event detection task. Firstly， to relieve the problem of insufficient data， various data augmentation methods such as back-translation and Mix-Text were applied to augment data and create weakly supervised learning scene for event detection. And then an adversarial training mechanism was applied to learn with noise and improve the robustness of the whole model. Several experiments were conducted on commonly used real-world dataset Automatic Context Extraction （ACE） 2005. The results show that compared with algorithms such as Nugget Proposal Network （NPN）， Trigger-aware Lattice Neural Network （TLNN） and Hybrid-Character-Based Neural Network （HCBNN）， the proposed method has the F1 score improved by at least 0.84 percentage points.