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Method for the Machine-Generation of Ancient Chinese Poetry Based on a Deep Neural Network Combined with Images

The use of deep learning techniques to solve problems in the art world has become a challenge for researchers. This paper uses deep learning technology to study and analyze the generation of ancient Chinese poetry as an art form. The results of early methods have the following problems: the poems are not clear, the sentences are inconsistent, and the readability is not smooth. This paper proposes a method incorporating image features to automatically generate ancient Chinese poems. First, ResNet and long short-term memory are used to obtain the Chinese description from the given image feature. Then, the keywords from the Chinese description are extracted from the sentence containing the creative intent and combined into the keyword sequence and the pregeneration sequence. Finally, these sequences are used to constrain the length and duration of the memory network to generate ancient Chinese poetry. The proposed method is evaluated using a manual evaluation method. The poetry generated by our proposed method achieves good coherency, fluency, and meaning, resulting in a high average score, which indicates that our method is effective. Thus, the task of generating ancient Chinese poetry is accomplished.