Recognizing predicate verbs is the key to understanding sentences. Because Chinese predicate verbs are complex in structure, flexible in use, and changeable in form, identifying predicate verbs is a challenging task in Chinese natural language processing. This article introduces the concepts related to the recognition of Chinese predicate verbs from the perspective of information extraction, and proposes a method for marking Chinese predicate verbs. On this basis, a Chinese predicate verb recognition method based on Attentional-BiLSTM-CRF neural network is studied. This method uses the bidirectional recurrent neural network to obtain the dependency relationship within the sentence, and then uses the attention mechanism to model the focus role of the sentence. Finally, a maximized labeling path through the conditional random field(CRF)layer is returned. In addition, in order to solve the problem of the uniqueness of predicate verb output, a unique recognition model of predicate verb based on convolutional neural network is proposed. Through experiments, the algorithm exceeds the traditional sequence labeling model CRF, and reaches an F value of 76.75% on the Chinese predicate verb data labeled in this paper.