Analyzing and capturing the spirit in the historic Tang Dynasty poems for creating a machine that can compose new poetry is a difficult but fun challenge. In this research, we propose a rhyming knowledge-aware deep neural network for Chinese poetry generation. The model fuses rhyming knowledge that represents phonological tones into a long short-term memory (LSTM) model. This work will help us understand more about what kind of mechanism within the neural network contributes to different styles of the generated poems. The experimental results demonstrate that the proposed method is able to guide the style of those poems towards higher phonological compliance, fluency, coherence, and meaningfulness, as evaluated by human experts. We believe that future research can adopt our approach to further integrate more knowledge such as sentiments, POS, and even stylistic patterns found in poems by famous poets into poem generation.