A lot of prior work on event extraction has exploited a variety of features to represent events. Such methods have several drawbacks: 1) the features are often specific for a particular domain and do not generalize well; 2) the features are derived from various linguistic analyses and are error-prone; and 3) some features may be expensive and require domain expert. In this paper, we develop a Chinese event extraction system that uses word embedding vectors to represent language, and deep neural networks to learn the abstract feature representation in order to greatly reduce the effort of feature engineering. In addition, in this framework, we leverage large amount of unlabeled data, which can address the problem of limited labeled corpus for this task. Our experiments show that our proposed method performs better compared to the system using rich language features, and using unlabeled data benefits the word embeddings. This study suggests the potential of DNN and word embedding for the event extraction task.