[Objective] This paper proposed a model to extract semantic features from texts more comprehensively and to improve the representation of semantics by text vectors. [Methods] We obtained the word-granularity, topic-granularity and character-granularity feature vectors with the help of convolutional neural networks. Then, the three feature vectors were combined by the “merging gate” mechanism to generate the final text vectors. Finally, we examined the model with text classification experiment. [Results] The accuracy (92.56%), the precision (92.33%), the recall (92.07%) and the F-score (92.20%), were 2.40%, 2.05%, 1.77% and 1.91% higher than the results of Text-CNN. [Limitations] The Long-distance dependency features need to be included and the corpus size needs to be expanded. [Conclusions] The proposed model could better represent the text semantics.
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