With the increasing popularity of social networks, sentiment analysis based on Twitter text has become a hotspot in recent years. The sentiment tendencies contained in tweets are important for mining user needs and predicting major events. However, the existing sentiment classification methods are mostly based on hand-made text features, and it is hard to mine implicit deep semantics of texts. In addition, because of special characteristics, such as short text and arbitrariness of users' behavior, it is more difficult to improve performance of current sentiment classification. This paper presents a novel Twitter sentiment classification model based on convolutional neural network (CNN). In order to explore sentiment tendency of tweets, the proposed model utilizes a dynamic CNN architecture to learn deep semantics from tweets, which initializes input word embedding with word2vec method. Experimental results show that our proposed model can achieve a recall rate of 82.3%, which is much higher than performances of traditional classification methods.