Event coreference resolution is more difficult than entity coreference resolution. The main reason is that the event mentions in the unstructured texts are sparse, and most of them do not have the coreference relationship, at the same time, the semantic information carried by the event itself is richer than entity. In order to accurately extract the coreferential events in the text, for the above characteristics of event coreference resolution, an event coreference resolution platform with text representation is proposed. This platform effectively distinguishes non-event mention, single-chain and coreference event mention through CRF, and uses hierarchical attention mechanism to capture important information at sentence level and text level. Experiments on KBP2015 and 2016 datasets verify the validity of the model, and the CoNLL evaluation standard reaches 43.07% of the F1 value.