Aiming at the problems that the current word segmentation tool can not effectively distinguish all medical terms in Chinese medical field, and feature engineering has high labor cost, a multi-scale Convolutional Neural Network (CNN) modeling method based on attention mechanism and character embedding was proposed. In the proposed method, character embedding was combined with multi-scale CNN to extract context information at different scales of question and answer sentences, and attention mechanism was introduced to emphasize the interaction between question sentences and answer sentences, meanwhile the semantic relationship between the question sentence and the correct answer sentence was able to be effectively learned. Since the question and answer matching task in Chinese medical field does not have a standard evaluation dataset, the proposed method was evaluated using the publicly available Chinese Medical Question and Answer dataset (cMedQA). The experimental results show that the proposed method is superior to word matching, character matching and Bi-directional Long Short-Term Memory network (BiLSTM) modeling method, and the Top-1 accuracy is 65.43%.