In this paper, we develop a convolutional neural network for stance detection in tweets. According to the official results, our system ranks 1 st on subtask B (among 9 teams) and ranks 2 nd on subtask A (among 19 teams) on the twitter test set of SemEval2016 Task 6. The main contribution of our work is as follows. We design a "vote scheme" for prediction instead of predicting when the accuracy of validation set reaches its maximum. Besides, we make some improvement on the specific subtasks. For

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... subtask A, we separate datasets into five sub-datasets according to their targets, and train and test five separate models. For subtask B, we establish a two-class training dataset from the official domain corpus, and then modify the softmax layer to perform three-class classification. Our system can be easily re-implemented and optimized for other related tasks.