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Literary therapy theory provides a new field for contemporary literature research and is of great significance for maintaining the physical and mental health of modern people. The quantitative evaluation of psychotherapy effects in Japanese healing literature is a hot research topic at present. In this study, a text convolutional neural network (Text-CNN) was selected to extract psychological therapy features with different levels of granularity by using multiple convolutional kernels of different sizes. Bidirectional threshold regression neural network (BiGRU) can characterize the relationship between literature research and the psychotherapy effect. On the basis of the CNN-BilSTM model, a parallel hybrid network integrated with the attention mechanism was constructed to analyze the correlation between literature and psychotherapy. Through experimental verification, the model in this study further improves the accuracy of correlation classification and has strong adaptability.