The emotional curve of a story is the core embodiment of the reading value of a novel, and good novels tend to have similar patterns of emotional changes, which are explored in novels by combining artificial intelligence technology. After collecting modern Chinese novel texts, Chinese word segmentation and de-duplication are performed to complete the novel text preprocessing. In view of the limitations of convolutional neural network (CNN) and recurrent neural network (RNN) in text feature extraction, this paper proposes a multi-channel convolutional and bi-directionally gated recurrent unit (BiGRU) deep learning model, Pt-MCBGA, to mine the emotional polarity in the text and analyze the emotional trend of modern Chinese novels. After a series of comparison experiments, it is demonstrated that the model performance achieves a relatively excellent performance, and the recall rate on the two datasets is improved to 83.53% and 83.69%, respectively. According to the Pt-MCBGA model, the sentiment analysis of the modern Chinese novel The Legend of the Eagle Shooting Heroes finds that the novel is dominated by positive sentiment, with both positive and negative sentiment values being relatively high, and that the characters are rich in emotions and have great emotional ups and downs.