Neural network-based architectures have been pervasively applied to sentiment analysis and achieved great success in recent years. However, most previous approaches usually classified with word feature only, which ignoring some characteristic features on the task of sentiment classification. One of the remaining challenges is to leverage the sentiment resources effectively because of the lack of length of Chinese micro-blog texts. To address this problem, we propose a novel sentiment classification method for Chinese micro-blog sentiment analysis based on multi-channels convolutional neural networks (MCCNN) to capture the characteristic information in micro-blog texts. With the help of the part of speech vector, the model could promote the full use of sentiment features through different part of speech tagging. Meanwhile, the position vector helps the model indicate the degree of importance of every word in the sentence, which impels the model to focus on the important words in the training process. Afterwards, a multi-channels architecture based on convolutional neural networks will be used to learn more feature information of micro-blog texts, and extract more hidden information through combining different vectors and original word embedding. Finally, the experiments on COAE2014 dataset and micro-blog dataset reveal better performance than the current main stream convolutional neural networks and traditional classifier.