This paper tackles a new task for event entity recognition (EER). Different from named entity recognizing (NER) task it only identifies the named entities which are related to a specific event type. Currently there is no specific model to directly deal with the EER task. Previous namedentity recognition methods that combine both relation extraction and argument role classification(named NER+TD+ARC) can be adapted for the task by utilizing the relation extraction component for event trigger detection (TD). However these technical alternatives heavily rely on the efficiency of the event trigger detection which have to require the tedious yet expensive human la-beling of the event triggers especially for languages where triggers contain multiple tokens andhave numerous synonymous expressions (such as Chinese). In this paper a novel trigger-awaremulti-task learning framework (TAM) which jointly performs both trigger detection and evententity recognition is proposed to tackle Chinese EER task. We conduct extensive experimentson a real-world Chinese EER dataset. Compared with the previous methods TAM outperformsthe existing technical alternatives in terms of F1 measure. Besides TAM can accurately identifythe synonymous expressions that are not included in the trigger dictionary. Morover TAM canobtain a robust performance when only a few labeled triggers are available.