In view of the lack of feature attributes and the granularity division on emotion intensity level in Chinese film reviews,a fine-grained sentiment classification model based on ontological features was proposed. Firstly,Term Frequency-Inverse Document Frequency(TF-IDF)and TextRank algorithm were used to extract movie features and construct ontology conceptual model. Secondly,the film attributes and Plutchik's Wheel of Emotion were combined with Bidirectional Long Short-Term Memory (Bi-LSTM) neural network to build a fine-grained emotion classification model based on feature granularity level and eight-category emotion intensity. In the experiments,the analysis of ontological features shows that the movie viewers pay the most attention to the attributes of the story,followed by the features of theme,character,scene and director;Model performance analysis shows that,based on feature granularity and eight-category emotion intensity, compared with other five classification models using emotion dictionary,machine learning and Bi-LSTM network algorithm at the level of overall granularity and three-category emotion intensity,the proposed model not only has a higher F1 value (0. 93),but also can provide viewers with a reference to emotional preferences and emotional intensities of film attributes, and achieves a more fine-grained emotional classification of Chinese film reviews.