School of Journalism and Communication, Jilin Normal University, Changchun 130021, Jilin, China.
Comput Intell Neurosci. 2022 May 20;2022:1685430. doi: 10.1155/2022/1685430. eCollection 2022.
As the continous innovation of new media technology, the media environment of the entire society has undergone profound changes. Digital technology has had a profound impact on the way news is disseminated. It has made a significant impact on the collecting, creation, and distribution of news, as well as the way viewers receive it. As a result, the news media's operation and management style is continually modified. However, in the process of news dissemination, the situations involved are complex and changeable, which leads to different digital technology applications. Aiming at different complex situations in news dissemination under the vision of new media art, this work designs a neural network to optimize the distribution for the required digital technology application schemes. The main work of this paper has the following two points. First, it systematically investigates the current research status of news communication based on digital technology and analyzes the research trends of digital technology and news communication in complex contexts under the vision of new media art. Second, a new neural network is proposed for the optimal application of digital technology for news propagation in different complex situations. This neural network uses an improved particle swarm optimization algorithm and an improved network training strategy to improve the BP network, which can effectively solve the shortcomings of the BP network. A large number of experiments have proved the effectiveness and correctness of this method.
随着新媒体技术的不断创新，整个社会的媒体环境发生了深刻的变化。数字技术对新闻传播方式产生了深远的影响，对新闻的采集、创作和传播方式以及观众的接收方式都产生了重大影响。因此，新闻媒体的运营管理方式在不断地修改。然而，在新闻传播过程中，所涉及的情况复杂多变，导致数字技术的应用也各不相同。针对新媒体艺术视野下新闻传播中不同的复杂情况，本工作设计了一个神经网络来优化所需数字技术应用方案的分配。本文的主要工作有以下两点。首先，系统地研究了基于数字技术的新闻传播的现状，分析了新媒体艺术视野下复杂环境下数字技术与新闻传播的研究趋势。其次，针对不同复杂情况下的新闻传播提出了一种新的神经网络，用于优化数字技术的应用。该神经网络使用改进的粒子群优化算法和改进的网络训练策略对 BP 网络进行改进，可有效解决 BP 网络的缺点。大量实验证明了该方法的有效性和正确性。
