Event extraction aims to extract the interesting and structured information from unstructured text. Most Chinese event extraction methods use a continuous pipeline model which first identify event trigger word, and then identify the event arguments. Thus, it is prone to produce cascading errors, and the information contained in downstream task cannot be fed back to the upstream task. In this study, event extraction is considered as a sequence labeling task, and a multi-task learning with CRF enhanced Chinese event extraction model is proposed. Two extensions on the CRF based event extraction model are performed:(1) the separate training strategy to solve multi-label problem for an event argument in the joint model (i.e., when an event scope includes multiple events, the same entity tends to play different roles in different events); (2) considered event arguments of sub-events under the same class have the high correlation, a multi-task learning approach is proposed to jointly learn sub-events, which can alleviate the corpus sparsity to some extent. The experiment results on ACE 2005 Chinese corpus show the effectiveness of the proposed method.