  Chinese traditional poetry is an important intangible cultural heritage of
China and an artistic carrier of thought, culture, spirit and emotion. However,
due to the strict rules of ancient poetry, it is very difficult to write poetry
by machine. This paper proposes an automatic generation method of Chinese
traditional poetry based on deep learning technology, which extracts keywords
from each poem and matches them with the previous text to make the poem conform
to the theme, and when a user inputs a paragraph of text, the machine obtains
the theme and generates poem sentence by sentence. Using the classic word2vec
model as the preprocessing model, the Chinese characters which are not
understood by the computer are transformed into matrix for processing.
Bi-directional Long Short-Term Memory is used as the neural network model to
generate Chinese characters one by one and make the meaning of Chinese
characters as accurate as possible. At the same time, TF-IDF and TextRank are
used to extract keywords. Using the attention mechanism based encoding-decoding
model, we can solve practical problems by transforming the model, and
strengthen the important information of long-distance information, so as to
grasp the key points without losing important information. In the aspect of
emotion judgment, Long Short-Term Memory network is used. The final result
shows that it can get good poetry outputs according to the user input text.
