# The Application of Digital Technology in the Complex Situation of News Dissemination from the Perspective of New Media Art

Authors: Xunxun Jiang; Tong Mao; Jing Tian
Journal: Computational Intelligence and Neuroscience
 (2022)

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## Abstract

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.


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## Body

## 1. Introduction

Digital technology and information science are at the heart of new media. On the basis of the philosophy of mass communication and the creative techniques of modern art, its various domains, including science and art, business and education, culture and the arts, and management, are all integrated through the application of information technology’s all-encompassing capabilities. A wide range of media forms are included in new media. New media art’s distinguishing features are its communication medium and the digitalization of its disseminating content. Digitization fulfills the creative needs of new media artists by facilitating the collection, access, processing, and sharing of data. It has become a new information carrier in the post-language era, after text and electronic technology. During this period, researchers began to use computer technology for image processing and creation of sound works. At this time, the forms of digital art showed diversified forms, from static to dynamic interactive works, from creating a virtual world to reflect the initiative of the real world. The development of digital technology makes new media art challenge traditional concepts. Therefore, new media art is a comprehensive media art and performance art that allows the audience to participate [1–5].News dissemination refers to mass dissemination activities carried out through the news media. It includes not only news gathering, editing, transmission, and reception activities organized by traditional mass media, but also news dissemination activities through the Internet and mobile phones and other digital new media. Books, video games, CD/VCD/DVD, MP3/MP4 music, and other communication methods in the mass media are temporarily not included in the category of news media, because they have nothing to do with the dissemination of real-time news information. Auxiliary news media such as news agencies and photo agencies will not discuss them temporarily, because they do not directly address a broad audience [6–10].News communication has always been closely related to development of technology; communication technology is the first driving force for the development of communication, because it determines the renewal of the media, promotes the transformation of communication methods, and leads to the evolution of communication concepts and the development of high-energy journalists. Everywhere we go in the history of news reporting we see the traces of technical progress. Modern electronic technology has made it possible for news dissemination to outpace text delivery in terms of speed and efficiency. This has entered a new field of sound and image dissemination, further expanding the geographical scope and number of audiences of news dissemination. There has been a dramatic shift in the way news is disseminated since the introduction of digital technologies and the subsequent advancement of computer, multimedia, communication, and network technologies. The digitization and networking of news dissemination have changed the existence of news dissemination, the transmission content, and the temporal and spatial relationship of human activities and will create a broader space for news dissemination [11–15].With the complexity of news dissemination situation, the digital technology required in the dissemination process is also different. For different communication situations, how to design the optimal digital technology application program is an important and novel topic in the process of news communication. With the development for computer science, neural networks can efficiently model this task. Therefore, this article designs a neural network based on computer neural network technology. It can optimize and customize digital technology application schemes for the complex situations of news dissemination under the vision of new media art.The contributions of this article are as follows: (1) this article applies neural network to the complex situation of news dissemination, which is used to customize the optimal digital technology application scheme in this situation. (2) For the customization of digital technology application schemes, this paper designs an artificial neural network. This network combines the improved particle swarm algorithm with BP network and optimizes the training strategy of BP network accordingly. This can effectively improve the customization performance of digital technology applications.


## 2. Related Work

Foreign research in this area had been carried out relatively early. Sociologists and technical experts began to study this issue and achieved some results. Literatures [16–19] discussed the application of digital technology and outlined a rough outline for the future information society. Since the development of digital technology was still in its infancy at that time, these works had very little to do with the application of digital technology in news dissemination. It was not until the next few years that the literature [20] clearly put forward the argument that “the world of media has changed.” And in the book, the concepts of digital TV, multimedia, virtual reality, and so on were mentioned. Other countries with rapid development of digital technology had also produced a number of research results on emerging online media. It mainly included literatures [21–23] and so on.Before the mid-1990s, related research in my country was progressing slowly. After the mid-1990s, with the gradual advancement of my country’s digitalization process, related foreign academic works had been translated and published successively, such as the related documents mentioned above. Some domestic scholars had begun to get involved in this research field and had achieved great research results. This was mainly manifested in the following: (1) related works and papers continued to emerge. Literature [24] analyzed the content of papers published in Journalism and Communication Research, International Journalism, and Journalism University from 1996 to the end of the 20th century. It found that articles related to media technical analysis showed an overall increasing trend, increasing from the previous single-digit percentage to more than 25%. A large number of relevant research articles had been published on the online platform “China Journalism and Communication Review” jointly established by Sina.com, Zhejiang Online, and the School of Journalism and Communication of Zhejiang University. (2) Among the papers presented at large-scale academic conferences held by the journalism and communication circles in my country, the proportion of papers related to the research on new technologies of journalism and communication had risen sharply. The School of Journalism and Communication of Tsinghua University held a seminar with the theme of “Media Reform in the Digital Background.” The central issue discussed in this seminar was the impact of digital technology on media. The participating experts discussed the latest development trends of digital media, the behavior and characteristics of people using new media, and various issues related to this. (3) The National Social Science Fund was also very concerned about this field. Since 1996, related topics had been established almost every year, such as the 1996 project “Multimedia Technology and News Dissemination,” the 1998 project “Research Report on the New Development of Network Communication and Its Countermeasures,” and “Analysis and Development Forecast of the Digital Status Quo of News Dissemination Means.” The 8 projects established in 2000 included “Research on the Influence of the Internet on Information Dissemination and People’s Spiritual and Cultural Life” [25, 26]. Among relevant domestic researches, the more influential works included literatures [27–30] and so on.In the era of digital information, news dissemination tools had achieved a high degree of uniformity, and computers and networks had become important tools for processing and transmitting news information. In the digital age, traditional writing tools and transportation tools were no longer needed for the processing and transmission of information. Computers and networks had completely replaced them. Moreover, the information carrier of news dissemination had also been completely unified. Any information object, whether it was numbers, words, or symbols, or sounds, graphics, or images, only needed to be carried by bits. At the same time, digital information for news dissemination on the Internet is open, and people’s acceptance and consumption of information had also shifted from a passive state in the materialized information age to a more active and interactive state. Therefore, it could be said that digital technology had brought a new stage of news dissemination. At this stage, the essence of news dissemination had not changed, and the function of news dissemination in monitoring the environment, coordinating society, and transmitting cultural heritage had not changed much. But the way and content of news dissemination were constantly changing. Literature [31] believed that the content of one communication tool was often another communication tool. The content of writing was speech, just as handwriting was the content of printing, and printing was the content of telegram. Literature [32] pointed out that when technology used symbolic signs or found a place in a special social structure, it became a medium, a social and intellectual resource created by a machine.


## 3. Method

This article uses neural networks to specify the best digital technology solutions for different news dissemination scenarios called DTPDN. For example, when the content of news dissemination is sound, and the media is broadcasting, voice signal processing is the most suitable digital technology. When the content of news dissemination is video, and the transmission medium is TV, video processing technology is the most suitable digital technology. In short, this article is to use neural network to model these attribute parameters through the parameter attributes of various complex situations under the vision of new media art and finally output the most optimized digital technology solution. The model used in this article is BP neural network, and it is improved by PSO and training optimization strategy. This can make the neural network more accurate in the formulation of digital technology solutions. The structure is illustrated in Figure1.Figure 1
The structure of DTPTN.


### 3.1. Improved PSO

The classic BP network model’s ability to forecast outcomes is not perfect. There is a reason for this: the BP network algorithm’s initial threshold and weight parameters are often arbitrarily set. A local minimum state of the BP network is easy to enter, which results in low model prediction accuracy. This chapter uses an improved particle swarm optimization approach to optimize the initial parameters of the BP neural network model, which successfully increases the model’s accuracy.


#### 3.1.1. Particle Swarm Optimization Algorithm

For population assumed by particle swarm optimization, each particle is abstracted as an individual that can move independently and has a certain speed. In the objective function’s solution space, each particle is a solution that is constantly moving. The initial speed and initial direction of the particle flight can be randomly generated as the initial state of each individual. And based on the overall flight experience in the population and the individual's own flight experience to jointly determine the flight plan and status of the next iteration, in the actual problem to be solved by the PSO algorithm, it is necessary to determine the optimized objective function, also called the fitness function. Each individual particle judges and evaluates its own flight search effect according to the value of the fitness function. During the flight search process, the point where each individual encounters the optimal fitness function value is the optimal solution pBest. In the same way, by comparing the individual optimal pBest found by all the individual particles, the global optimal gBest can be compared. In the continuous iterative operation of PSO, each individual particle dynamically adjusts its flight search plan based on individual optimal pBest and global optimal gBest. Such a process is actually similar to the evolutionary process of species. Because each iteration produces a better population, PSO algorithm is also called evolutionary algorithm. The basis of PSO is to use the collective wisdom of the population to conduct a global search and compare local optimal solutions according to the fitness function. These processes do not need to derive the objective function, nor do they need to have too many assumptions and conventions.The population is composed of some abstract individual particles. Each individual has no mass or volume, and the number of individuals is the dimension of the population. Assume that the dimension for a certain group isn, the position of the i-th particle in the group is a vector Xi=x1,x2,…,xn, and the flying speed of the particle is a vector Vi=v1,v2,…,vn. In PSO, each individual particle maintains the optimal value of the individual searched by itself. Then, in the process of individual particle flight search, it is necessary to compare with the global optimal value to judge and update its position. Its essence is to exchange global search result information, which is also the global search capability of particle swarm algorithm:(1)vt+1=vt+a1rpbestt−pt+a2rgbestt−pt,pt+1=pt+vt+1,where r is a random function, vt is the velocity expression of the particle, and a1 and a2 represent the learning speed of particles.In actual optimization applications, it is often hoped that the particles can have a larger acceleration when the standard particle swarm optimization method is just started, thereby improving the global search ability. When the population obtains certain information, the search space is quickly converged to a smaller range. Then, reduce the particle’s movement speed, so that the particle can be searched finely in the local area. Therefore, a nonnegative inertia weight is added to the particle's velocity term to control the speed of the current particle’s velocity transformation:(2)vt+1=wvt+a1rpbestt−pt+a2rgbestt−pt,where w is the inertia weight. The larger the inertia weight, the greater the potential for global optimization, and the lesser the ability for local optimization. The lower the value, the weaker the ability of global optimization, and the better the ability of local optimization. Dynamically changing the inertia weight can obtain better optimization ability than that of the fixed inertia weight. The inertia weight can change linearly during the particle swarm search process, or it can be dynamically changed according to a certain measurement function of the particle swarm performance. At present, the linearly decreasing weight strategy is widely used:(3)w=ws−weGk−gGk+we,where Gk is the maximum number of iterations, ws is the initial value of the inertia factor, and we is value of the inertia factor when the population is iterated to the maximum number of evolutions.The advantages of particle swarm optimization algorithm are extremely significant. (1) The superiority of the idea of swarm intelligence is that it is not easy to fall into a local optimization, and it has the ability to search and optimize globally. (2) The population converges to the global optimum fast. (3) The search of each particle is parallel, except when exchanging information; the movement between the particles can be regarded as unrelated to each other, and they are all moving on the path they think is correct. (4) There are no special assumptions and restrictions on the input data, which makes the particle swarm optimization algorithm suitable for solving almost all optimization problems. (5) There is almost no need to modify any parameters; you can input into the model. (6) The standard particle swarm algorithm has a simple idea and does not have high requirements on the input data itself. The computational time complexity of PSO is much lower than that of the genetic algorithm.


#### 3.1.2. Improvement Strategy

The standard particle swarm optimization algorithm converges to a local extremum and cannot escape when it is calculated and solved. Because every time the population exchanges information, the search scope is narrowed. At this time, it is possible that the particle has not searched all the regions, or it is too late to find the optimal solution. The search area is narrowed down by the population, resulting in the optimal solution being excluded from the search range, and it can only converge to a local extremum. Many documents have studied this problem, but the nature of the problem has not changed, and the scope of the global search has been narrowed down. Therefore, this article attempts to improve this problem. When each particle searches for its own extreme value, it is compared with the extreme value found by population. If the extremum searched by the population is better than the extremum searched by the particle itself, the standard particle swarm method strategy is to update position and coordinate vector of the particle to the position and coordinates of the extreme point found by the population. At this time, the search range of the population will be narrowed down to the vicinity of the extreme point. However, the strategy adopted in this paper is to add random variation factors after the population finds the extreme points. After the particle position and coordinates are updated, the global search range is still maintained. It avoids the global optimal solution being excluded from the search area. At the same time, modify the adaptive function and modify the conditions for the particles to end the search, so that the particles can finally exit the iteration. The improved PSO in this paper can expand the search range of particles in the solution space, so that the particles have a certain random distribution in the global search range. This makes the algorithm converge to the optimal solution faster, while also ensuring that it will not converge locally.To overcome the premature convergence, it is easy to fall local optimum. After the optimal information is exchanged, a dynamic position change is added when narrowing the search range. This allows the particles to have different random search capabilities at different stages. At the same time, some checking mechanisms are added to determine whether the particles have a tendency to locally converge. This can adjust the movement speed and position of the particles in time, so that the entire particle swarm can continue to maintain a faster convergence rate. This will not sacrifice the global optimization capability of PSO itself. The specific design is as follows.The variation factor of the particle is a binary random function with respect to the number of iterations and time. Use the number of iterations to consider, and add time as an independent variable. When the population is initialized, timet is 0 at this time. And within a certain period of time, no mutation occurs. Therefore, the significance of the independent variable of time is to control the timing of mutation. At the same time, the consideration of the number of iterations is that if iterations reached a certain value, the population still does not converge, and then mutation is turned on. This allows the particles to have a global search range.But the fitness converges to a certain value, and the number of times of convergence reaches a certain number of times. At this time, it may be converged to the local optimum, and mutation is turned on at this time. This can help particles escape from local extreme points.When the position of the particle is always in a certain area, and it is difficult to escape, it is considered that the population has the possibility of converging to the local optimum, and mutation can be turned on. The specific mutation factor operation is to modify the position of the particles with formula of standard PSO:(4)pt+1=P×r×pt,where P is the core probability factor, which determines the intensity of variation in the next iteration of the particle.According to the improved ideas and design of PSO, the specific implementation steps of improved PSO (IPSO) are as follows: (1) initialize particle swarm and initialize dimension of the population. The initial velocity, initial position, and velocity extremes of individual particles are initialized to the optimal value of each individual particle. Set the fitness function and the maximum iterations to ensure that the algorithm can finally end. (2) According to global optimal value and individual optimal value, update the speed and position of each individual in the population, and judge whether the speed of each particle is out of bounds. If the boundary is exceeded, the velocity threshold is used to limit the velocity of the particles. (3) Update the global optimal value of the population based on the individual optimal value of all individuals in the population. (4) According to the differences in speed and location of the population before and after evolution, if the population has the possibility of converging to a local optimum, compare the iterations with the number for convergences to the local. According to the dynamic variation formula, the position of the particles is adjusted dynamically and has a certain uniform distribution in the whole world. (5) If the end condition is not reached, then go to step 2. (6) The algorithm operation ends. The final judgment condition is that the fitness of the particles meets the expected error range or the algorithm encounters an abnormality. This adaptive mutation will be called when the position update and speed update of the particles in the population are used to increase diversity of population and also maintain a better convergence speed.


### 3.2. Training Optimization Strategy

The training of BP uses error backpropagation method. The change direction of the weight threshold depends on the change direction of the error function. This method begins to decline rapidly, but when the error function enters a flatter curve, it changes slowly and falls into a local extreme. Aiming at this defect, this paper proposes an improved BP method and designs an improved additional momentum algorithm and an improved adaptive learning rate adjustment method.


#### 3.2.1. Improved Additional Momentum Algorithm

The standard BP algorithm only relies on a simple static optimization method to modify the weight and does not consider the gradient direction at the previous time. As a result, the network converges slowly and oscillates during the learning process. Literature [33] proposed a BP learning algorithm to increase the momentum term. The weight change in this method is obtained by summing the appropriate ratio of the current negative gradient change of the error surface and the weight change used in the previous iteration correction. The weight adjustment formula of the additional momentum term is(5)Δwijn+1=−1−αβ∂E∂wij+αΔwijn,Δbijn+1=−1−αβ∂E∂bij+αΔbijn,where α is the momentum factor.In order to increase learning speed, the momentum term can store the value of a change in the weight of the link at the previous time. Additionally, the added momentum component acts as a buffer and smoother function. During training, the inertial effect can be used to reduce the occurrence of oscillations. It is also advantageous for escaping the flat terrain because of the additional momentum period. If the training has entered the flat area of error surface, thenwn+1=Δwn, thus preventing the situation of Δwn+1=0, making the model jump out of the local minimum. To put it simply, the momentum component added in the additional momentum term technique is essentially a damping term that can help buffer and smooth out the learning process, minimizing the oscillation trend and therefore enhancing the network’s converged performance. The addition of a momentum term can effectively reduce the network’s susceptibility to local error surface features. It can also be effectively controlled when the network falls into a local minimum. However, when the error surface becomes steeper, it becomes difficult to select the learning rate. To solve this problem, many works have conducted research and proposed some solutions, for example, the adaptive learning rate adjustment method. Although this method improves the difficulty of learning rate selection to a certain extent, it still has the problem of slow learning rate update. In order to update the additional momentum in time, the judgment conditions of the improved use weight correction formula designed in this paper are(6)α=0,En+13>1.04En3,0.95,En+13<En3,α,others.Judging by this condition, the value of additional momentum is updated. To follow up on the change of the error in time, adjust the direction of the change of the weight threshold.


#### 3.2.2. Improved Adaptive Learning Rate

It is impossible to change the conventional BP algorithm’s learning rate after it has been established. BP neural network learning process is slowed down by this issue. The selection of the learning rate should not be too wide-ranging. The network may not be able to converge if the learning rate is set too high and oscillates throughout the training period. In other words, if the rate of learning is too low, the convergence speed cannot be guaranteed. It is difficult to make major modifications in a reasonably flat error surface, because some sections of the BP network error surface are uneven. Even if the adjustment amount of the weight is increased, it is difficult to reduce the error quickly. In this case, a larger learning rate is required. The gradient of the error changes very rapidly in a relatively steep area, and a smaller learning rate needs to be selected at this time.This paper proposes an improved method of adaptively adjusting the learning rate. As a result, the normal BP algorithm’s convergence characteristics are improved by this algorithm’s ability to adaptively adjust its learning rate to manage the BP neural network’s gradient descent speed in learning. It is vital to maintain a large learning rate in order to provide stability in the learning process, which is based on particular principles. The learning rate adaptive adjustment formula is(7)βn+1=1.05βn,En+13<En3,0.7βn,En+13>1.04En3,βn,others.The learning rate is automatically adjusted based on error. When the error becomes a certain multiple compared with the previous error, the learning rate will become smaller. Otherwise, the learning rate remains unchanged. If the error becomes smaller, increase the learning rate. This method can keep the learning rate at the maximum acceptable value throughout the training process. The learning rate is updated quickly to ensure that the network is always trained at the maximum acceptable learning rate. This method can dynamically process the network, and it can make the learning rate reasonable, so as to speed up convergence speed. This can ensure the stability of BP network and the convergence of BP algorithm.


## 3.1. Improved PSO

The classic BP network model’s ability to forecast outcomes is not perfect. There is a reason for this: the BP network algorithm’s initial threshold and weight parameters are often arbitrarily set. A local minimum state of the BP network is easy to enter, which results in low model prediction accuracy. This chapter uses an improved particle swarm optimization approach to optimize the initial parameters of the BP neural network model, which successfully increases the model’s accuracy.


### 3.1.1. Particle Swarm Optimization Algorithm

For population assumed by particle swarm optimization, each particle is abstracted as an individual that can move independently and has a certain speed. In the objective function’s solution space, each particle is a solution that is constantly moving. The initial speed and initial direction of the particle flight can be randomly generated as the initial state of each individual. And based on the overall flight experience in the population and the individual's own flight experience to jointly determine the flight plan and status of the next iteration, in the actual problem to be solved by the PSO algorithm, it is necessary to determine the optimized objective function, also called the fitness function. Each individual particle judges and evaluates its own flight search effect according to the value of the fitness function. During the flight search process, the point where each individual encounters the optimal fitness function value is the optimal solution pBest. In the same way, by comparing the individual optimal pBest found by all the individual particles, the global optimal gBest can be compared. In the continuous iterative operation of PSO, each individual particle dynamically adjusts its flight search plan based on individual optimal pBest and global optimal gBest. Such a process is actually similar to the evolutionary process of species. Because each iteration produces a better population, PSO algorithm is also called evolutionary algorithm. The basis of PSO is to use the collective wisdom of the population to conduct a global search and compare local optimal solutions according to the fitness function. These processes do not need to derive the objective function, nor do they need to have too many assumptions and conventions.The population is composed of some abstract individual particles. Each individual has no mass or volume, and the number of individuals is the dimension of the population. Assume that the dimension for a certain group isn, the position of the i-th particle in the group is a vector Xi=x1,x2,…,xn, and the flying speed of the particle is a vector Vi=v1,v2,…,vn. In PSO, each individual particle maintains the optimal value of the individual searched by itself. Then, in the process of individual particle flight search, it is necessary to compare with the global optimal value to judge and update its position. Its essence is to exchange global search result information, which is also the global search capability of particle swarm algorithm:(1)vt+1=vt+a1rpbestt−pt+a2rgbestt−pt,pt+1=pt+vt+1,where r is a random function, vt is the velocity expression of the particle, and a1 and a2 represent the learning speed of particles.In actual optimization applications, it is often hoped that the particles can have a larger acceleration when the standard particle swarm optimization method is just started, thereby improving the global search ability. When the population obtains certain information, the search space is quickly converged to a smaller range. Then, reduce the particle’s movement speed, so that the particle can be searched finely in the local area. Therefore, a nonnegative inertia weight is added to the particle's velocity term to control the speed of the current particle’s velocity transformation:(2)vt+1=wvt+a1rpbestt−pt+a2rgbestt−pt,where w is the inertia weight. The larger the inertia weight, the greater the potential for global optimization, and the lesser the ability for local optimization. The lower the value, the weaker the ability of global optimization, and the better the ability of local optimization. Dynamically changing the inertia weight can obtain better optimization ability than that of the fixed inertia weight. The inertia weight can change linearly during the particle swarm search process, or it can be dynamically changed according to a certain measurement function of the particle swarm performance. At present, the linearly decreasing weight strategy is widely used:(3)w=ws−weGk−gGk+we,where Gk is the maximum number of iterations, ws is the initial value of the inertia factor, and we is value of the inertia factor when the population is iterated to the maximum number of evolutions.The advantages of particle swarm optimization algorithm are extremely significant. (1) The superiority of the idea of swarm intelligence is that it is not easy to fall into a local optimization, and it has the ability to search and optimize globally. (2) The population converges to the global optimum fast. (3) The search of each particle is parallel, except when exchanging information; the movement between the particles can be regarded as unrelated to each other, and they are all moving on the path they think is correct. (4) There are no special assumptions and restrictions on the input data, which makes the particle swarm optimization algorithm suitable for solving almost all optimization problems. (5) There is almost no need to modify any parameters; you can input into the model. (6) The standard particle swarm algorithm has a simple idea and does not have high requirements on the input data itself. The computational time complexity of PSO is much lower than that of the genetic algorithm.


### 3.1.2. Improvement Strategy

The standard particle swarm optimization algorithm converges to a local extremum and cannot escape when it is calculated and solved. Because every time the population exchanges information, the search scope is narrowed. At this time, it is possible that the particle has not searched all the regions, or it is too late to find the optimal solution. The search area is narrowed down by the population, resulting in the optimal solution being excluded from the search range, and it can only converge to a local extremum. Many documents have studied this problem, but the nature of the problem has not changed, and the scope of the global search has been narrowed down. Therefore, this article attempts to improve this problem. When each particle searches for its own extreme value, it is compared with the extreme value found by population. If the extremum searched by the population is better than the extremum searched by the particle itself, the standard particle swarm method strategy is to update position and coordinate vector of the particle to the position and coordinates of the extreme point found by the population. At this time, the search range of the population will be narrowed down to the vicinity of the extreme point. However, the strategy adopted in this paper is to add random variation factors after the population finds the extreme points. After the particle position and coordinates are updated, the global search range is still maintained. It avoids the global optimal solution being excluded from the search area. At the same time, modify the adaptive function and modify the conditions for the particles to end the search, so that the particles can finally exit the iteration. The improved PSO in this paper can expand the search range of particles in the solution space, so that the particles have a certain random distribution in the global search range. This makes the algorithm converge to the optimal solution faster, while also ensuring that it will not converge locally.To overcome the premature convergence, it is easy to fall local optimum. After the optimal information is exchanged, a dynamic position change is added when narrowing the search range. This allows the particles to have different random search capabilities at different stages. At the same time, some checking mechanisms are added to determine whether the particles have a tendency to locally converge. This can adjust the movement speed and position of the particles in time, so that the entire particle swarm can continue to maintain a faster convergence rate. This will not sacrifice the global optimization capability of PSO itself. The specific design is as follows.The variation factor of the particle is a binary random function with respect to the number of iterations and time. Use the number of iterations to consider, and add time as an independent variable. When the population is initialized, timet is 0 at this time. And within a certain period of time, no mutation occurs. Therefore, the significance of the independent variable of time is to control the timing of mutation. At the same time, the consideration of the number of iterations is that if iterations reached a certain value, the population still does not converge, and then mutation is turned on. This allows the particles to have a global search range.But the fitness converges to a certain value, and the number of times of convergence reaches a certain number of times. At this time, it may be converged to the local optimum, and mutation is turned on at this time. This can help particles escape from local extreme points.When the position of the particle is always in a certain area, and it is difficult to escape, it is considered that the population has the possibility of converging to the local optimum, and mutation can be turned on. The specific mutation factor operation is to modify the position of the particles with formula of standard PSO:(4)pt+1=P×r×pt,where P is the core probability factor, which determines the intensity of variation in the next iteration of the particle.According to the improved ideas and design of PSO, the specific implementation steps of improved PSO (IPSO) are as follows: (1) initialize particle swarm and initialize dimension of the population. The initial velocity, initial position, and velocity extremes of individual particles are initialized to the optimal value of each individual particle. Set the fitness function and the maximum iterations to ensure that the algorithm can finally end. (2) According to global optimal value and individual optimal value, update the speed and position of each individual in the population, and judge whether the speed of each particle is out of bounds. If the boundary is exceeded, the velocity threshold is used to limit the velocity of the particles. (3) Update the global optimal value of the population based on the individual optimal value of all individuals in the population. (4) According to the differences in speed and location of the population before and after evolution, if the population has the possibility of converging to a local optimum, compare the iterations with the number for convergences to the local. According to the dynamic variation formula, the position of the particles is adjusted dynamically and has a certain uniform distribution in the whole world. (5) If the end condition is not reached, then go to step 2. (6) The algorithm operation ends. The final judgment condition is that the fitness of the particles meets the expected error range or the algorithm encounters an abnormality. This adaptive mutation will be called when the position update and speed update of the particles in the population are used to increase diversity of population and also maintain a better convergence speed.


## 3.1.1. Particle Swarm Optimization Algorithm

For population assumed by particle swarm optimization, each particle is abstracted as an individual that can move independently and has a certain speed. In the objective function’s solution space, each particle is a solution that is constantly moving. The initial speed and initial direction of the particle flight can be randomly generated as the initial state of each individual. And based on the overall flight experience in the population and the individual's own flight experience to jointly determine the flight plan and status of the next iteration, in the actual problem to be solved by the PSO algorithm, it is necessary to determine the optimized objective function, also called the fitness function. Each individual particle judges and evaluates its own flight search effect according to the value of the fitness function. During the flight search process, the point where each individual encounters the optimal fitness function value is the optimal solution pBest. In the same way, by comparing the individual optimal pBest found by all the individual particles, the global optimal gBest can be compared. In the continuous iterative operation of PSO, each individual particle dynamically adjusts its flight search plan based on individual optimal pBest and global optimal gBest. Such a process is actually similar to the evolutionary process of species. Because each iteration produces a better population, PSO algorithm is also called evolutionary algorithm. The basis of PSO is to use the collective wisdom of the population to conduct a global search and compare local optimal solutions according to the fitness function. These processes do not need to derive the objective function, nor do they need to have too many assumptions and conventions.The population is composed of some abstract individual particles. Each individual has no mass or volume, and the number of individuals is the dimension of the population. Assume that the dimension for a certain group isn, the position of the i-th particle in the group is a vector Xi=x1,x2,…,xn, and the flying speed of the particle is a vector Vi=v1,v2,…,vn. In PSO, each individual particle maintains the optimal value of the individual searched by itself. Then, in the process of individual particle flight search, it is necessary to compare with the global optimal value to judge and update its position. Its essence is to exchange global search result information, which is also the global search capability of particle swarm algorithm:(1)vt+1=vt+a1rpbestt−pt+a2rgbestt−pt,pt+1=pt+vt+1,where r is a random function, vt is the velocity expression of the particle, and a1 and a2 represent the learning speed of particles.In actual optimization applications, it is often hoped that the particles can have a larger acceleration when the standard particle swarm optimization method is just started, thereby improving the global search ability. When the population obtains certain information, the search space is quickly converged to a smaller range. Then, reduce the particle’s movement speed, so that the particle can be searched finely in the local area. Therefore, a nonnegative inertia weight is added to the particle's velocity term to control the speed of the current particle’s velocity transformation:(2)vt+1=wvt+a1rpbestt−pt+a2rgbestt−pt,where w is the inertia weight. The larger the inertia weight, the greater the potential for global optimization, and the lesser the ability for local optimization. The lower the value, the weaker the ability of global optimization, and the better the ability of local optimization. Dynamically changing the inertia weight can obtain better optimization ability than that of the fixed inertia weight. The inertia weight can change linearly during the particle swarm search process, or it can be dynamically changed according to a certain measurement function of the particle swarm performance. At present, the linearly decreasing weight strategy is widely used:(3)w=ws−weGk−gGk+we,where Gk is the maximum number of iterations, ws is the initial value of the inertia factor, and we is value of the inertia factor when the population is iterated to the maximum number of evolutions.The advantages of particle swarm optimization algorithm are extremely significant. (1) The superiority of the idea of swarm intelligence is that it is not easy to fall into a local optimization, and it has the ability to search and optimize globally. (2) The population converges to the global optimum fast. (3) The search of each particle is parallel, except when exchanging information; the movement between the particles can be regarded as unrelated to each other, and they are all moving on the path they think is correct. (4) There are no special assumptions and restrictions on the input data, which makes the particle swarm optimization algorithm suitable for solving almost all optimization problems. (5) There is almost no need to modify any parameters; you can input into the model. (6) The standard particle swarm algorithm has a simple idea and does not have high requirements on the input data itself. The computational time complexity of PSO is much lower than that of the genetic algorithm.


## 3.1.2. Improvement Strategy

The standard particle swarm optimization algorithm converges to a local extremum and cannot escape when it is calculated and solved. Because every time the population exchanges information, the search scope is narrowed. At this time, it is possible that the particle has not searched all the regions, or it is too late to find the optimal solution. The search area is narrowed down by the population, resulting in the optimal solution being excluded from the search range, and it can only converge to a local extremum. Many documents have studied this problem, but the nature of the problem has not changed, and the scope of the global search has been narrowed down. Therefore, this article attempts to improve this problem. When each particle searches for its own extreme value, it is compared with the extreme value found by population. If the extremum searched by the population is better than the extremum searched by the particle itself, the standard particle swarm method strategy is to update position and coordinate vector of the particle to the position and coordinates of the extreme point found by the population. At this time, the search range of the population will be narrowed down to the vicinity of the extreme point. However, the strategy adopted in this paper is to add random variation factors after the population finds the extreme points. After the particle position and coordinates are updated, the global search range is still maintained. It avoids the global optimal solution being excluded from the search area. At the same time, modify the adaptive function and modify the conditions for the particles to end the search, so that the particles can finally exit the iteration. The improved PSO in this paper can expand the search range of particles in the solution space, so that the particles have a certain random distribution in the global search range. This makes the algorithm converge to the optimal solution faster, while also ensuring that it will not converge locally.To overcome the premature convergence, it is easy to fall local optimum. After the optimal information is exchanged, a dynamic position change is added when narrowing the search range. This allows the particles to have different random search capabilities at different stages. At the same time, some checking mechanisms are added to determine whether the particles have a tendency to locally converge. This can adjust the movement speed and position of the particles in time, so that the entire particle swarm can continue to maintain a faster convergence rate. This will not sacrifice the global optimization capability of PSO itself. The specific design is as follows.The variation factor of the particle is a binary random function with respect to the number of iterations and time. Use the number of iterations to consider, and add time as an independent variable. When the population is initialized, timet is 0 at this time. And within a certain period of time, no mutation occurs. Therefore, the significance of the independent variable of time is to control the timing of mutation. At the same time, the consideration of the number of iterations is that if iterations reached a certain value, the population still does not converge, and then mutation is turned on. This allows the particles to have a global search range.But the fitness converges to a certain value, and the number of times of convergence reaches a certain number of times. At this time, it may be converged to the local optimum, and mutation is turned on at this time. This can help particles escape from local extreme points.When the position of the particle is always in a certain area, and it is difficult to escape, it is considered that the population has the possibility of converging to the local optimum, and mutation can be turned on. The specific mutation factor operation is to modify the position of the particles with formula of standard PSO:(4)pt+1=P×r×pt,where P is the core probability factor, which determines the intensity of variation in the next iteration of the particle.According to the improved ideas and design of PSO, the specific implementation steps of improved PSO (IPSO) are as follows: (1) initialize particle swarm and initialize dimension of the population. The initial velocity, initial position, and velocity extremes of individual particles are initialized to the optimal value of each individual particle. Set the fitness function and the maximum iterations to ensure that the algorithm can finally end. (2) According to global optimal value and individual optimal value, update the speed and position of each individual in the population, and judge whether the speed of each particle is out of bounds. If the boundary is exceeded, the velocity threshold is used to limit the velocity of the particles. (3) Update the global optimal value of the population based on the individual optimal value of all individuals in the population. (4) According to the differences in speed and location of the population before and after evolution, if the population has the possibility of converging to a local optimum, compare the iterations with the number for convergences to the local. According to the dynamic variation formula, the position of the particles is adjusted dynamically and has a certain uniform distribution in the whole world. (5) If the end condition is not reached, then go to step 2. (6) The algorithm operation ends. The final judgment condition is that the fitness of the particles meets the expected error range or the algorithm encounters an abnormality. This adaptive mutation will be called when the position update and speed update of the particles in the population are used to increase diversity of population and also maintain a better convergence speed.


## 3.2. Training Optimization Strategy

The training of BP uses error backpropagation method. The change direction of the weight threshold depends on the change direction of the error function. This method begins to decline rapidly, but when the error function enters a flatter curve, it changes slowly and falls into a local extreme. Aiming at this defect, this paper proposes an improved BP method and designs an improved additional momentum algorithm and an improved adaptive learning rate adjustment method.


### 3.2.1. Improved Additional Momentum Algorithm

The standard BP algorithm only relies on a simple static optimization method to modify the weight and does not consider the gradient direction at the previous time. As a result, the network converges slowly and oscillates during the learning process. Literature [33] proposed a BP learning algorithm to increase the momentum term. The weight change in this method is obtained by summing the appropriate ratio of the current negative gradient change of the error surface and the weight change used in the previous iteration correction. The weight adjustment formula of the additional momentum term is(5)Δwijn+1=−1−αβ∂E∂wij+αΔwijn,Δbijn+1=−1−αβ∂E∂bij+αΔbijn,where α is the momentum factor.In order to increase learning speed, the momentum term can store the value of a change in the weight of the link at the previous time. Additionally, the added momentum component acts as a buffer and smoother function. During training, the inertial effect can be used to reduce the occurrence of oscillations. It is also advantageous for escaping the flat terrain because of the additional momentum period. If the training has entered the flat area of error surface, thenwn+1=Δwn, thus preventing the situation of Δwn+1=0, making the model jump out of the local minimum. To put it simply, the momentum component added in the additional momentum term technique is essentially a damping term that can help buffer and smooth out the learning process, minimizing the oscillation trend and therefore enhancing the network’s converged performance. The addition of a momentum term can effectively reduce the network’s susceptibility to local error surface features. It can also be effectively controlled when the network falls into a local minimum. However, when the error surface becomes steeper, it becomes difficult to select the learning rate. To solve this problem, many works have conducted research and proposed some solutions, for example, the adaptive learning rate adjustment method. Although this method improves the difficulty of learning rate selection to a certain extent, it still has the problem of slow learning rate update. In order to update the additional momentum in time, the judgment conditions of the improved use weight correction formula designed in this paper are(6)α=0,En+13>1.04En3,0.95,En+13<En3,α,others.Judging by this condition, the value of additional momentum is updated. To follow up on the change of the error in time, adjust the direction of the change of the weight threshold.


### 3.2.2. Improved Adaptive Learning Rate

It is impossible to change the conventional BP algorithm’s learning rate after it has been established. BP neural network learning process is slowed down by this issue. The selection of the learning rate should not be too wide-ranging. The network may not be able to converge if the learning rate is set too high and oscillates throughout the training period. In other words, if the rate of learning is too low, the convergence speed cannot be guaranteed. It is difficult to make major modifications in a reasonably flat error surface, because some sections of the BP network error surface are uneven. Even if the adjustment amount of the weight is increased, it is difficult to reduce the error quickly. In this case, a larger learning rate is required. The gradient of the error changes very rapidly in a relatively steep area, and a smaller learning rate needs to be selected at this time.This paper proposes an improved method of adaptively adjusting the learning rate. As a result, the normal BP algorithm’s convergence characteristics are improved by this algorithm’s ability to adaptively adjust its learning rate to manage the BP neural network’s gradient descent speed in learning. It is vital to maintain a large learning rate in order to provide stability in the learning process, which is based on particular principles. The learning rate adaptive adjustment formula is(7)βn+1=1.05βn,En+13<En3,0.7βn,En+13>1.04En3,βn,others.The learning rate is automatically adjusted based on error. When the error becomes a certain multiple compared with the previous error, the learning rate will become smaller. Otherwise, the learning rate remains unchanged. If the error becomes smaller, increase the learning rate. This method can keep the learning rate at the maximum acceptable value throughout the training process. The learning rate is updated quickly to ensure that the network is always trained at the maximum acceptable learning rate. This method can dynamically process the network, and it can make the learning rate reasonable, so as to speed up convergence speed. This can ensure the stability of BP network and the convergence of BP algorithm.


## 3.2.1. Improved Additional Momentum Algorithm

The standard BP algorithm only relies on a simple static optimization method to modify the weight and does not consider the gradient direction at the previous time. As a result, the network converges slowly and oscillates during the learning process. Literature [33] proposed a BP learning algorithm to increase the momentum term. The weight change in this method is obtained by summing the appropriate ratio of the current negative gradient change of the error surface and the weight change used in the previous iteration correction. The weight adjustment formula of the additional momentum term is(5)Δwijn+1=−1−αβ∂E∂wij+αΔwijn,Δbijn+1=−1−αβ∂E∂bij+αΔbijn,where α is the momentum factor.In order to increase learning speed, the momentum term can store the value of a change in the weight of the link at the previous time. Additionally, the added momentum component acts as a buffer and smoother function. During training, the inertial effect can be used to reduce the occurrence of oscillations. It is also advantageous for escaping the flat terrain because of the additional momentum period. If the training has entered the flat area of error surface, thenwn+1=Δwn, thus preventing the situation of Δwn+1=0, making the model jump out of the local minimum. To put it simply, the momentum component added in the additional momentum term technique is essentially a damping term that can help buffer and smooth out the learning process, minimizing the oscillation trend and therefore enhancing the network’s converged performance. The addition of a momentum term can effectively reduce the network’s susceptibility to local error surface features. It can also be effectively controlled when the network falls into a local minimum. However, when the error surface becomes steeper, it becomes difficult to select the learning rate. To solve this problem, many works have conducted research and proposed some solutions, for example, the adaptive learning rate adjustment method. Although this method improves the difficulty of learning rate selection to a certain extent, it still has the problem of slow learning rate update. In order to update the additional momentum in time, the judgment conditions of the improved use weight correction formula designed in this paper are(6)α=0,En+13>1.04En3,0.95,En+13<En3,α,others.Judging by this condition, the value of additional momentum is updated. To follow up on the change of the error in time, adjust the direction of the change of the weight threshold.


## 3.2.2. Improved Adaptive Learning Rate

It is impossible to change the conventional BP algorithm’s learning rate after it has been established. BP neural network learning process is slowed down by this issue. The selection of the learning rate should not be too wide-ranging. The network may not be able to converge if the learning rate is set too high and oscillates throughout the training period. In other words, if the rate of learning is too low, the convergence speed cannot be guaranteed. It is difficult to make major modifications in a reasonably flat error surface, because some sections of the BP network error surface are uneven. Even if the adjustment amount of the weight is increased, it is difficult to reduce the error quickly. In this case, a larger learning rate is required. The gradient of the error changes very rapidly in a relatively steep area, and a smaller learning rate needs to be selected at this time.This paper proposes an improved method of adaptively adjusting the learning rate. As a result, the normal BP algorithm’s convergence characteristics are improved by this algorithm’s ability to adaptively adjust its learning rate to manage the BP neural network’s gradient descent speed in learning. It is vital to maintain a large learning rate in order to provide stability in the learning process, which is based on particular principles. The learning rate adaptive adjustment formula is(7)βn+1=1.05βn,En+13<En3,0.7βn,En+13>1.04En3,βn,others.The learning rate is automatically adjusted based on error. When the error becomes a certain multiple compared with the previous error, the learning rate will become smaller. Otherwise, the learning rate remains unchanged. If the error becomes smaller, increase the learning rate. This method can keep the learning rate at the maximum acceptable value throughout the training process. The learning rate is updated quickly to ensure that the network is always trained at the maximum acceptable learning rate. This method can dynamically process the network, and it can make the learning rate reasonable, so as to speed up convergence speed. This can ensure the stability of BP network and the convergence of BP algorithm.


## 4. Experiments and Discussion

### 4.1. Dataset

This article uses a self-built dataset for experimentation. The input of this dataset contains four first-level indicators of news dissemination situation, and each first-level indicator contains several second-level indicators. The specific indicator is illustrated in Table1.Table 1
Parameter index of news dissemination situation.
First-levelSecond-levelNews dissemination subjectGovernmentOrganization/institutionIndividualNews dissemination mediumRadioTelevisionInternetNews dissemination contentSoundImageVideoNews dissemination audienceOld peopleMiddle-aged peopleYoung peopleThis article quantifies these indicators as the input of network, and the output is the corresponding digital technology application program, such as voice processing technology and video processing technology. The self-made dataset in this article contains a total of 16,392 pieces of data, of which 80% is training set and 20% is testing set. The evaluation indexes applied in this work are precision, recall, and F1 score.


### 4.2. Evaluation on Training Convergence

The convergence of network and performance of the convergence are two critical performance measures in the neural network. Studying training loss and precision is used to determine if this is a contributing element. Results are illustrated in Figure2.Figure 2
Training loss and training accuracy.The training loss lowers, and the training precision increases as the network training advances. Network convergence occurs when the number of iterations exceeds 100. Network reliability and robustness may be ensured by achieving convergent network training with our proposed algorithm.


### 4.3. Comparison with Additional Methods

In order to determine the success of our deigned method, we compare this model to other methods such as logistic regression (LR), decision tree (DT), random forest (RF), and support vector machine (SVM). Precision, recall, and the F1 score are some of the comparison metrics. The experimental result is illustrated in Table2.Table 2
Comparison with additional methods.
MethodPrecisionRecallF1LR0.8270.8088.811DT0.8520.8430.847RF0.8860.8590.864SVM0.9030.8770.893DTPDN (ours)0.9280.9040.915It is not difficult to see that, compared to other similar methods, our method can obtain the highest performance on the three evaluation indicators. Specifically, 0.928 precision, 0.904 recall, and 0.915 F1 score can be obtained, respectively. Compared with the best method listed in the table, our method can obtain 2.5% precision gain, 2.7% recall gain, and 2.2% F1 score gain. This shows that our method is able to formulate accurate digital technology application plans for various complex communication environments under the new media art perspective. These data illustrate the effectiveness and reliability of our method.


### 4.4. Evaluation on IPSO

As mentioned earlier, this paper designs an IPSO algorithm for the problem that BP networks tend to fall local optimal solutions to assign values to the initial weights and thresholds for networks. To verify the effectiveness, this work will compare the performance of not using genetic optimization algorithm, using traditional PSO, and using IPSO. The experimental results are shown in Figure3.Figure 3
Evaluation on IPSO.Obviously, whether it is during network training or testing, the performance of the traditional BP network is the lowest. But when the PSO algorithm is used, the performance of the network has been improved to a certain extent, and the improvement is limited. If the PSO algorithm is improved, the IPSO algorithm is constructed. After combining it with the BP network, a further performance improvement can be obtained. Compared with the traditional PSO algorithm, the testing performance can be improved by 4.3%, 3.4%, and 2.5%. This proves the correctness and effectiveness of IPSO and can improve the accuracy of the formulation of the optimal digital technology application program in the news dissemination of complex situations.


### 4.5. Evaluation on Training Optimization Strategy

As mentioned earlier, the training of BP neural network usually adopts the error backpropagation method. The change direction of the weight threshold depends on the change direction of the error function. In this method, the loss drops rapidly when the network is first trained, but when the error function enters a flatter curve, the change is slow. Aiming at this defect, this paper proposes an improved BP algorithm and designs an improved additional momentum (IAM) algorithm and an improved adaptive learning (IALRD) rate adjustment method. To verify the effectiveness of this method, this work will compare the performance of not using training optimization strategy, using traditional additional momentum (AM) algorithm with adaptive learning (ALRD) rate adjustment method, and using improved additional momentum (IAM) algorithm with improved adaptive learning (IALRD) rate adjustment method. The experimental results are illustrated in Figure4.Figure 4
Evaluation on training optimization strategy.Obviously, whether it is during network training or testing, the performance of the traditional BP network is the lowest. But when the training optimization strategy with improvement is used, the performance of the network has been improved. But the improvement is limited. If the training optimization strategy is improved, after combining it with the BP network, a further performance improvement can be obtained. Compared to traditional optimization, the testing performance can be improved by 3.2%, 2.3%, and 2.5%. This proves the correctness and effectiveness of the designed training optimization strategy in this paper and can improve accuracy of formulation of optimal digital technology application program in the news dissemination of complex situations.


## 4.1. Dataset

This article uses a self-built dataset for experimentation. The input of this dataset contains four first-level indicators of news dissemination situation, and each first-level indicator contains several second-level indicators. The specific indicator is illustrated in Table1.Table 1
Parameter index of news dissemination situation.
First-levelSecond-levelNews dissemination subjectGovernmentOrganization/institutionIndividualNews dissemination mediumRadioTelevisionInternetNews dissemination contentSoundImageVideoNews dissemination audienceOld peopleMiddle-aged peopleYoung peopleThis article quantifies these indicators as the input of network, and the output is the corresponding digital technology application program, such as voice processing technology and video processing technology. The self-made dataset in this article contains a total of 16,392 pieces of data, of which 80% is training set and 20% is testing set. The evaluation indexes applied in this work are precision, recall, and F1 score.


## 4.2. Evaluation on Training Convergence

The convergence of network and performance of the convergence are two critical performance measures in the neural network. Studying training loss and precision is used to determine if this is a contributing element. Results are illustrated in Figure2.Figure 2
Training loss and training accuracy.The training loss lowers, and the training precision increases as the network training advances. Network convergence occurs when the number of iterations exceeds 100. Network reliability and robustness may be ensured by achieving convergent network training with our proposed algorithm.


## 4.3. Comparison with Additional Methods

In order to determine the success of our deigned method, we compare this model to other methods such as logistic regression (LR), decision tree (DT), random forest (RF), and support vector machine (SVM). Precision, recall, and the F1 score are some of the comparison metrics. The experimental result is illustrated in Table2.Table 2
Comparison with additional methods.
MethodPrecisionRecallF1LR0.8270.8088.811DT0.8520.8430.847RF0.8860.8590.864SVM0.9030.8770.893DTPDN (ours)0.9280.9040.915It is not difficult to see that, compared to other similar methods, our method can obtain the highest performance on the three evaluation indicators. Specifically, 0.928 precision, 0.904 recall, and 0.915 F1 score can be obtained, respectively. Compared with the best method listed in the table, our method can obtain 2.5% precision gain, 2.7% recall gain, and 2.2% F1 score gain. This shows that our method is able to formulate accurate digital technology application plans for various complex communication environments under the new media art perspective. These data illustrate the effectiveness and reliability of our method.


## 4.4. Evaluation on IPSO

As mentioned earlier, this paper designs an IPSO algorithm for the problem that BP networks tend to fall local optimal solutions to assign values to the initial weights and thresholds for networks. To verify the effectiveness, this work will compare the performance of not using genetic optimization algorithm, using traditional PSO, and using IPSO. The experimental results are shown in Figure3.Figure 3
Evaluation on IPSO.Obviously, whether it is during network training or testing, the performance of the traditional BP network is the lowest. But when the PSO algorithm is used, the performance of the network has been improved to a certain extent, and the improvement is limited. If the PSO algorithm is improved, the IPSO algorithm is constructed. After combining it with the BP network, a further performance improvement can be obtained. Compared with the traditional PSO algorithm, the testing performance can be improved by 4.3%, 3.4%, and 2.5%. This proves the correctness and effectiveness of IPSO and can improve the accuracy of the formulation of the optimal digital technology application program in the news dissemination of complex situations.


## 4.5. Evaluation on Training Optimization Strategy

As mentioned earlier, the training of BP neural network usually adopts the error backpropagation method. The change direction of the weight threshold depends on the change direction of the error function. In this method, the loss drops rapidly when the network is first trained, but when the error function enters a flatter curve, the change is slow. Aiming at this defect, this paper proposes an improved BP algorithm and designs an improved additional momentum (IAM) algorithm and an improved adaptive learning (IALRD) rate adjustment method. To verify the effectiveness of this method, this work will compare the performance of not using training optimization strategy, using traditional additional momentum (AM) algorithm with adaptive learning (ALRD) rate adjustment method, and using improved additional momentum (IAM) algorithm with improved adaptive learning (IALRD) rate adjustment method. The experimental results are illustrated in Figure4.Figure 4
Evaluation on training optimization strategy.Obviously, whether it is during network training or testing, the performance of the traditional BP network is the lowest. But when the training optimization strategy with improvement is used, the performance of the network has been improved. But the improvement is limited. If the training optimization strategy is improved, after combining it with the BP network, a further performance improvement can be obtained. Compared to traditional optimization, the testing performance can be improved by 3.2%, 2.3%, and 2.5%. This proves the correctness and effectiveness of the designed training optimization strategy in this paper and can improve accuracy of formulation of optimal digital technology application program in the news dissemination of complex situations.


## 5. Conclusion

With the changes in society and new media art technology, the contemporary media environment has undergone profound changes. Moreover, the development and application of digital technology have brought tremendous changes to news dissemination under the vision of new media art. It has greatly improved every aspect of news dissemination and the way the audience accepts it. This factor keeps the development model of news media updated. In the process of news dissemination, the situations involved are complex and changeable, which directly leads to different applications of digital technology. In view of the different complex situations in news dissemination from the perspective of new media art, this paper proposes an improved artificial neural network to formulate the required digital technology application program. First, this article systematically examines the status quo of digital technology in news dissemination from the perspective of new media art. It also analyzes the research trend of digital technology and news communication in the complex context under the new media art vision. Secondly, this article proposes a new type of neural network to optimize the application of digital technology in news dissemination in different complex situations. The 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. Massive experiments have proved the effectiveness and correctness of this method.



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*Source: 1685430-2022-05-20.xml*