This paper aims to develop a feasible way to recognize the style of classical Chinese poetry with computers. To this end, the authors explored the connectionism in neuroscience, and explained the cognitive word embedding with the convolutional neural network (CNN). On the one hand, the genetic algorithm was adopted to extract keywords from traditional hand-labelled and selected information; on the other hand, a novel computer learning method was proposed based on text-to-image (T2I) CNN for big data. The proposed method was contrasted with the traditional genetic algorithm of naive Bayes and information gain. The experimental results show that our method achieved better classification accuracy with less human intervention than the traditional genetic algorithm. Hence, the CNNbased method is feasible on big data, both in theory and practice. This cross-disciplinary practice sheds light on stylistics, literature engineering, poetry cognition and neural network projects.

Convolutional Neural Network (CNN), Classical Chinese Poetry, Cognitive Engineering, Connectionism