Shen Jun, Jiang Leping
School of Foreign Languages, Changshu Institute of Technology, Suzhou, China.
The Institute for Sustainable Development, Macau University of Science and Technology, Macau, China.
Front Psychol. 2022 May 30;13:906952. doi: 10.3389/fpsyg.2022.906952. eCollection 2022.
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.
文学治疗理论为当代文学研究提供了一个新领域，对维护现代人的身心健康具有重要意义。日本治愈文学中心理治疗效果的定量评估是当前的一个热门研究课题。在本研究中，选择文本卷积神经网络（Text-CNN），通过使用不同大小的多个卷积核来提取不同粒度级别的心理治疗特征。双向阈值回归神经网络（BiGRU）可以表征文学研究与心理治疗效果之间的关系。在CNN-BilSTM模型的基础上，构建了一个集成注意力机制的并行混合网络，以分析文学与心理治疗之间的相关性。通过实验验证，本研究中的模型进一步提高了相关性分类的准确性，具有很强的适应性。
