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Deep neural networks can obtain effective hierarchical representations, which make them perform well in image recognition and speech recognition. However, the disadvantages of deep learning relying on large-scale annotation data also limit its development. To this end, a multi-layer convolutional neural network based on knowledge graph prior knowledge is proposed in this paper, which acquires text features from finer granularity, and using the existing prior knowledge which increases granularity information to enhance the semantic information of the text, and to reduce the dependence of the model on large-scale samples. At the same time, it can reduce the over-fitting problem of the model, and improve classification accuracy. This model performs well in several large data sets. By introducing prior knowledge to TextCNN, a classical deep neural model, it has also verified the validity of using prior knowledge through experiments.