Most of the current Chinese questions and answers matching technologies require word segmentation first， and the word segmentation problem of Chinese medical text requires maintenance of medical dictionaries to reduce the impact of segmentation errors on subsequent tasks. However， maintaining dictionaries requires a lot of manpower and knowledge， making word segmentation problem always be a great challenge. At the same time， the existing Chinese medical questions and answers matching methods all model the questions and the answers separately， and do not consider the relationship between the keywords contained in the questions and the answers respectively. Therefore， an Attention mechanism based Stack Convolutional Neural Network （Att-StackCNN） model was proposed to solve the problem of Chinese medical questions and answers matching. Firstly， character embedding was used to encode the questions and answers to obtain the respective character embedding matrices. Then， the respective feature attention mapping matrices were obtained by constructing the attention matrix using the character embedding matrices of the questions and answers. After that， Stack Convolutional Neural Network （Stack-CNN） model was used to perform convolution operation to the above matrices at the same time to obtain the respective semantic representations of the questions and answers. Finally， the similarity was calculated， and the max-margin loss was calculated by using the similarity to update the network parameters. On the cMedQA dataset， the Top-1 accuracy of proposed model was about 1 percentage point higher than that of Stack-CNN model and about 0.5 percentage point higher than that of Multi-CNNs model. Experimental results show that Att-StackCNN model can improve the matching effect of Chinese medical questions and answers.