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. 2022 Mar 25:13:781802.
doi: 10.3389/fpsyg.2022.781802. eCollection 2022.

The Psychology Analysis for Post-production of College Students' Short Video Communication Education Based on Virtual Image and Internet of Things

Wufeng Tang  1   2
Affiliations

The Psychology Analysis for Post-production of College Students' Short Video Communication Education Based on Virtual Image and Internet of Things

Wufeng Tang. Front Psychol. .

Abstract

To improve the understanding of film and television postproduction for college students in the era of intelligent media, a study is conducted on college students' short video communication education and audience psychology based on the rapid development of virtual image and the Internet of Things (IoT). Primarily, the collaborative filtering algorithm (CFA) is optimized and combined with the principle of Spark and Hadoop platforms as well as the IoT and virtual image technologies. Then, a hybrid computing model is proposed, and the two algorithms are improved and combined, with 90,000 network video records as data samples. Finally, the push accuracy of the hybrid algorithm and the traditional algorithm is calculated and compared, and based on this, a questionnaire survey on the audience psychology of short video production is carried out for college students. The results show that the time user of the combined algorithm is always at least 0.4 s faster than that of a single algorithm and the running speed of the algorithm with five nodes is nearly 80% higher than the algorithm with a single node. The Spark algorithm with multinode has good versatility in image recording and processing of large groups of college students. When processing more than 100,000 image records, the deviation values of Spark and Hadoop with a single node exceeded 1.1, but the deviation value of the hybrid algorithm was still lower than 1.1. With the increase of data volume, the deviation values of the three algorithms are increasing. Compared with the traditional CFA algorithm, the optimized algorithm has a higher speed in processing data and is more accurate in content pushing. From the questionnaire survey of college students, it is found that contemporary college students are not active in learning knowledge of virtual images. Hence, it is concluded that colleges must carry out relevant courses based on short video communication education and strengthen the short video communication education of college students. A reference is provided for the development of college students' short video communication education in the digital age.

Keywords: collaborative filtering; college students; film and television education; the Internet of Things; virtual image.

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Conflict of interest statement

The author declares that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

FIGURE 1
FIGURE 1
Network video user scale.
FIGURE 2
FIGURE 2
Virtual technology [virtual reality (VR)] helmet principle.
FIGURE 3
FIGURE 3
Augmented reality (AR) principle of reality enhancement technology.
FIGURE 4
FIGURE 4
The overall layout of the algorithm.
FIGURE 5
FIGURE 5
Spark algorithm flow.
FIGURE 6
FIGURE 6
The alternating least square (ALS) algorithm flowchart.
FIGURE 7
FIGURE 7
Algorithm principle based on user collaboration. (A) Group A video materials. (B) Group B video materials. (C) Group C video materials. (D) Group D video materials.
FIGURE 8
FIGURE 8
Hadoop structure.
FIGURE 9
FIGURE 9
Advantages of the Hadoop algorithm.
FIGURE 10
FIGURE 10
Investigation process.
FIGURE 11
FIGURE 11
Image data analysis time.
FIGURE 12
FIGURE 12
Deviations of different algorithms.
FIGURE 13
FIGURE 13
Data processing rates of different nodes.
FIGURE 14
FIGURE 14
Knowledge level of a virtual image.
FIGURE 15
FIGURE 15
Release of video review.
See this image and copyright information in PMC

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