Event recognition is critical to information extraction.To overcome limitations of the exiting event recognition approaches

we proposed an event recognition model based on deep learning (DL-ERM).Firstly

we acquired candidate words through a word segmentation system and classified them into five categories.Then

we selected six recognition feature layers and constructed corresponding feature representation rules to convert words into vector samples.Finally

we employed a deep belief network (DBN) to extract deep semantic features of words

and used a back propagation neural network to identify events.The results of experiments show that the maximum

F

-measure is 85.17%.Furthermore

we presented a hybrid-supervised DBN

which combines the unsupervised and supervi

sed learning.The novel DBN improves the recognition performance (89.2%

F

-measure) and effectively controls the training time (increased by 27.50%).