Exploration of the Educational Utility of National Film Using Deep Learning From the Positive Psychology Perspective - Zhaxi, Yangzhen; Xiang, Yueting; Zou, Jilin; Zhang, Fengrui (Frontiers in Psychology, 2022)
This exploration focuses on the Educational Utility (EDU-UTL) of national films using Deep Learning (DL) algorithms via Positive Psychology. As an art form leading China’s film and television industry, national films have attracted the interest of many domestic scholars. Meanwhile, researchers have employed various science and technologies to conduct in-depth research on national films to improve film artistic levels and EDU-UTL. Accordingly, this paper comprehensively studies the EDU-UTL of national films using Quality Learning (Q-Learning) combined with DL algorithms and Educational Psychology. Then, a deep Q-Learning psychological model is proposed based on the Convolutional Neural Network (CNN). Specifically, the CNN uses the H-hop matrix to represent each node, and each hop indicates the neighborhood information. The experiment demonstrates that CNN has a good effect on local feature acquisition, and the representation ability of the obtained nodes is also powerful. When K=300, the psychological factor Recall of Probability Matrix Decomposition Factorization, Collaborative Deep Learning, Stack Denoising Automatic Encoder, and CNN-based deep Q-Learning algorithm is 0.35, 0.71, 0.76, and 0.78, respectively. The results suggest that the CNN-based deep Q-Learning psychological model can enhance the EDU-UTL of national films and improve the efficiency of film education from a Positive Psychology perspective.