No abstract

This article focuses on the multidimensional construction of the multimedia network public opinion supervision mechanism, puts the research on the background of the era of big data, and based on the analysis and definition of the difference between network public opinion and network public opinion, deeply summarizes the network public opinion in the era of big data. New features analyze the opportunities and challenges faced by online public opinion in the era of big data. Based on the rational construction of the index system, this paper studies the multimedia network public opinion evaluation and prediction algorithm. Existing network public opinion assessment and prediction algorithms have shortcomings in capturing the characteristics of data sequences and the long-term dependence of data sequences, and the problems of overfitting and gradient disappearance may occur during training. Because of the above problems, based on the long-term and short-term memory network model, a regularized method is used to construct a multimedia network public opinion prediction model algorithm. This paper builds a multimedia network public opinion threat rating evaluation model based on the public opinion supervision prediction model and conducts analysis. The model constructed this time can not only improve the accuracy of public opinion assessment and prediction but also better avoid the problem of gradient disappearance and overfitting.

As the development of artificial intelligence (AI) technology, the deep-learning (DL)-based Virtual Reality (VR) technology, and DL technology are applied in human-computer interaction (HCI), and their impacts on modern film and TV works production and audience psychology are analyzed. In film and TV production, audiences have a higher demand for the verisimilitude and immersion of the works, especially in film production. Based on this, a 2D image recognition system for human body motions and a 3D recognition system for human body motions based on the convolutional neural network (CNN) algorithm of DL are proposed, and an analysis framework is established. The proposed systems are simulated on practical and professional datasets, respectively. The results show that the algorithm's computing performance in 2D image recognition is 7–9 times higher than that of the Open Pose method. It runs at 44.3 ms in 3D motion recognition, significantly lower than the Open Pose method's 794.5 and 138.7 ms. Although the detection accuracy has dropped by 2.4%, it is more efficient and convenient without limitations of scenarios in practical applications. The AI-based VR and DL enriches and expands the role and application of computer graphics in film and TV production using HCI technology theoretically and practically.

In order to solve the problem that Film text is difficult to recognize and difficult to handle in Film Internet of Things, a method that can effectively identify the content in Film text is sought. This paper uses the Mask RCNN algorithm with ResNet101 as the backbone network to establish a Film document image segmentation model.The optimal hyperparameters are: the shape ratio of the anchor frame is [0.5, 1, 3], the threshold for non-maximum suppression is 0.15, and the confidence level is 0.85. The F1 score obtained at this time is 0.8951. When these hyperparameters are substituted into the IOU of 0.8, the F1 score is 0.7417. According to the results of the Pattern Recognition Laboratory of the Chinese Academy of Sciences, this algorithm model ranked first with an IOU of 0.6. Under the premise that IOU is 0.8, it is ranked second, and the first is a non-end-to-end model with a single task. It can be seen that the adjustment of the hyperparameters and the training of the algorithm model are relatively successful.The experimental results show that the MASK RCNN can accurately identify all the formulas in the Film Text. MASK RCNN is significantly better at identifying small objects such as formulas in Film Text images than traditional fast cnn and faster cnn.