Sentiment analysis belongs to a classification technique with strong practical value, which can identify the hidden imagery in the text. In this paper, we study the sentiment tendency of words in Chinese language and literature texts, design a fuzzy ontology-based method for calculating textual sentiment tendency, build a textual sentiment tendency analysis model (SDNN) based on deep neural network and sentiment attention mechanism, and use the model in Chinese language and literature texts for empirical analysis. At 202 iterations, the Loss value of the SDNN model reaches the lowest value of 0.4626, and the corresponding accuracy, recall, and F1 values are 90.44%, 79.68%, and 84.72%, respectively, and tend to be stable. It indicates that the model has better classification performance and can be used in the task of analyzing the sentiment of Chinese language and literature texts. In addition, this paper explores the rhyme structure of character emotion vocabulary in Chinese language literary discourse, and takes Liu Zongyuan’s related works as an example for empirical analysis of the model. The results show that Liu Zongyuan’s emotion during his ten years in Yongzhou is generally in low, and the proportion of his negative emotion has been above 75%, which is reflected in the text vocabulary emotion is more sad than happy. This paper is of some significance for the study of the emotional tendency of vocabulary in Chinese language literary texts.