Event Extraction is an important task in natural language understanding, which aims to identify event trigger of pre-defined event types and their arguments of specific roles, has attracted a lot of attention from industry and academia. The previous works failed to address some issues, including error propagation problem, overlap and nest problem, and high complexity of model. This work proposes a novel model MLEE, which models Event Extraction task as Multi-Label classification task at token level, and processes the extraction task in a joint paradigm, can help solving issues mentioned above. The experiment verifies our model’s effectiveness. Empirical results on DuEE and FewFC shows that MLEE outperforms previous best model, pushing trigger extraction F1 to 85.03% (+4.45%), argument extraction F1 to 78.85% (+2.72%) on DuEE, pushing trigger extraction F1 to 76.69% (+1.63%), argument extraction F1 to 76.53% (+5.27%) on FewFC.