For Chinese question answer matching tasks, a question answer matching method based on deep learning was proposed to solve the problem of lack of features and low accuracy due to artificial structural feature in machine learning. This method mainly includes 3 different models. The first model is the combination of Recurrent Neural Network (RNN) and Convolutional Neural Network (CNN), which is used to learn the deep semantic features in the sentence and calculate the similarity distance of feature vectors. Moreover, adding two different attention mechanism into this model, the feature representation of answer was constructed according to the question to learn the detailed semantic matching relation of them. Experimental results show that the combined deep nerual network model is superior to the method of feature construction based on machine learning, and the hybrid model based on attention mechanism can further improve the matching accuracy where the best results can reach 80.05% and 68.73% in the standard evaluation of Mean Reciprocal Rank (MRR) and Top-1 accuracy respectively.