# Research on the Impact of Embedded Intelligent Robots on English News Dissemination

Authors: Yushu Xu; Weixing Gu
Journal: Wireless Communications and Mobile Computing
 (2022)

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## Abstract

With the rapid development of the Internet, although users can obtain information conveniently and quickly, they still cannot accurately obtain the target information they need in a short period of time in the face of massive information content. The use of embedded intelligent robots in the English news industry promotes the “Internet +” strategy, the goal of which is to connect everything. This article is aimed at studying the impact of embedded intelligent robots on the dissemination of English news. This article first analyzes embedded intelligent robots and news dissemination and then analyzes the collaborative filtering algorithm and text analysis technology of the recommendation system. Then, this article discusses the impact of embedded intelligent robot writing on English news dissemination and news media in the context of artificial intelligence.Direction of Development. Finally, in the experimental part, this paper implements a news recommendation system based on item recommendation algorithm and text analysis technology. Experiments have proved that the news recommendation algorithm of this system has a high hit rate and has great practical application value. This article sets each recommended page to 15 news, then randomly selects 5 users, and counts the number of news they visit, and the average number of news visited is 9.22. The results show that the embedded robot is helpful for English news dissemination, increasing the dissemination breadth by 20% and the dissemination accuracy by 30%.


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## Body

## 1. Introduction

The application of embedded intelligent robot technology in the news production process is more used as an auxiliary tool. In recent years, China has also conducted a series of useful investigations and attempts. Following the launch of Dream Writer’s new automated writing robot in China, Xinhua News Agency officially launched the writing robot “Kaipen” on November 17, 2015. In addition, at the Rio Olympics, Xiao Ming, a robot journalist jointly developed by Peking University and “Today’s Toutiao”, also made its debut and received widespread response and praise. Under the reputation of the era of smart media, the new production model of robot writing undoubtedly provides new directions and ideas for the reform of news and information dissemination. This not only brings huge opportunities for the transformation of traditional media but also opens up new ideas for digital media. But objectively speaking, robot writing still has many practical problems, and these problems have certain limitations.In addition to a comprehensive analysis of the current moral dilemmas in the practice of news dissemination, the study of the challenges of embedded intelligent robotics to news ethics and countermeasures also provides theoretical guidance for the better integration of embedded intelligent robotics with the media industry in the future. The journalistic ethics issues involved in the development of embedded intelligent robots cannot be ignored. When embedded intelligent robot products become more and more like human beings, where should the media practitioners go, how to change news production methods, these issues require journalistic ethics in the artificial intelligence environment. This study provides a theoretical basis. The application of embedded intelligence technology in the news field has caused many explicit and implicit news ethics issues. In order to solve the technical practical problems in the media industry activities, it is urgent to analyze the application status of embedded intelligent technology in the news field, combine the generated news ethical problems, and propose targeted solutions to prevent potential ethical risks, with a view to embedding. Realize the advantages and disadvantages in the application of intelligent technology. Technical flaws are not terrible. Continuously update and improve the technical level in news practice. This not only contributes to the long-term development of embedded intelligent robot technology but also helps to improve the effect of news dissemination and the healthy development of the entire society.Sernani and his team found that the world’s population is moving towards older people: according to recent estimates, by 2050, the world’s population aged 65 and over will reach 1.5 billion. Local governments, international agencies, care organizations, and the industry are encouraging the research community to find solutions to the aging population. The combination of artificial intelligence and NetMedicine may be ideal for these challenges: they provide a way to develop an intelligent system and simultaneously distribute it over a network and allow communication over the Internet when needed. Therefore, they proposed a multiagent architecture for environment-assisted living (AAL): it is a model for a system for managing a distributed sensor network composed of environmental sensors and biometric sensors. The system should analyze the data and proactively decide whether to trigger an alert when an anomaly is detected. They tested the belief-wish-intention (BDI) paradigm: a virtual caregiver, an architecture that implements a prototype multiagent system (MAS) [1]. Chen and Yu believe that collaborative filtering provides a solution for personalized recommendations to solve the problem of information overload. But data sparseness and scalability issues are serious factors affecting the quality of recommendations. To solve these problems, they proposed a collaborative filtering algorithm based on singular value decomposition and fuzzy clustering. In the special theory of relativity of physics, they retained the number of total eigenvalues through energy conservation theory, thereby determining the dimension of the reduced dimension. In addition, by using fuzzy clustering, they also reduced the search range of neighbors. Compared with the traditional collaborative filtering recommendation algorithm on different datasets of movie lens and 2013 Baidu movie recommendation system, this algorithm has better recommendation quality [2]. However, although the recommendation quality of this algorithm is good, the speed of recommendation needs to be further improved. Wang and his team believe that the main difficulty of credit risk assessment is to assess the borrower’s willingness to repay, which is a subjective factor that depends on the borrower’s thinking and ideas. Textual description is a human behavior that reflects the writer’s psychological process. They identify the characteristics of the borrower from the text description and further use it to assess the credit risk of the loan. They found textual information to be a good choice when traditional financial information was missing. They use textual information alone to obtain accuracy similar to traditional methods of using third-party financial and credit information. Text information is a good supplementary information source for traditional financial information sources. When used in combination with traditional financial information, the use of textual information can improve the performance of a credit risk assessment system [3].This paper studies the impact of robots on English news dissemination, and its advantages are as follows: (1) introduce the advantages of embedded robot technology, analyze news recommendation algorithms, and point out the importance of algorithms to news dissemination. (2) Research user preferences and recommend English news to users with the help of algorithms. (3) Design a news recommendation system based on item recommendation algorithm and text analysis technology and rely on artificial intelligence technology to expand the influence of English news.


## 2. Proposed Method

### 2.1. Embedded Intelligent Robots and News

The news industry’s proprietary embedded artificial intelligence technology and emerging technologies such as big data, cloud storage, and intelligent search have brought tremendous changes to the news industry [4]. The main applications of embedded artificial intelligence technology in the news industry are machine learning technology is used to make emotional judgments on interview topics and discover news clues from large amounts of data; automatically extract structured message summaries from natural language technology and unstructured messages; voice processing technology is applied to the voice interaction interface of news consumption, the automatic deletion of interview recordings, and the automatic quotation of video quotations [5, 6]. Figure 1 shows the structure of the intelligent robot platform.Figure 1
Intelligent robot platform structure.Visual information technology can be used in scenes such as face recognition and satellite high-resolution image recognition. Robots are used for automatic description, drone shooting from traditional angles, sensor data collection, and other scenarios. Artificial intelligence technology goes deep into the whole process of news production, news dissemination, and news monitoring. Figure2 shows the structure of the visual information system.Figure 2
Visual information system structure.Embedded intelligent robot technology is a new type of technology in recent years. Because robots contain a number of high-end technologies, they have been widely used in many fields and have received more and more attention. Today, robots are widely used in the medical field, military industry, education industry, and production and life. Although today’s intelligent robots seem to be very flexible, they were not so when they first appeared. Figure3 shows the intelligent robot distributed control system.Figure 3
Distributed control system for intelligent robots.In academic terms, we classify the earliest robots as the first-generation robots. The first-generation only repeats an action according to the program, which is not strictly a robot by current standards; the second-generation robot has been improved. Program control is transformed into computer control, getting rid of repetitive meaningless work, and many dangerous tasks can be completed according to program design. With the continuous development of science and technology and the needs of production, robots are gradually developing in the field of intelligence, first in appearance. It is very similar to human beings. It can use technology to identify the surrounding environment, detect its own state, analyze and process surrounding information, and predict the best solution. Intelligent robots can process data, which can greatly improve the efficiency of news production and achieve secondary response to emergencies. At the same time, embedded intelligent technology can quickly deliver machine news content to the algorithm recommendation platform, realizing automatic and accurate distribution and dissemination. It can be seen that news production in the era of information technology has greatly broken through the time and space constraints of news production in the editorial department and can complete the acquisition, editing, publishing, and even feedback processing of a large number of information sources in a short period of time. In recent years, embedded intelligent technology has been widely used in the production of social news. Many traditional media at home and abroad have begun to promote or complete the transformation of intelligent media. A large number of professional intelligent news production platforms are promoting the promotion of this production model. The process of combining embedded intelligent technology with news dissemination is shown in Figure4.Figure 4
Process of combining artificial intelligence and news communication.


### 2.2. Collaborative Filtering Recommendation Algorithm

(1)
Collaborative filtering algorithm based on itemsThe item-based collaborative filtering algorithm is the most commonly used algorithm in the industry [7]. The main idea is to use actions that the user has done before, recommending items similar to those that the user is interested in. The algorithm is based on the user first calculating the item similarity between items. Second, a user-oriented recommendation list is generated based on the similarity of the items.The collaborative filtering algorithm based on items has two parts: one is to calculate the similarity between the items, and the other is to generate a user’s recommendation list based on the similarity of the items and the user’s historical operations [8, 9]. The way the recommendation system associates users and items is shown in Figure 5.Figure 5
The way the recommendation system associates users and items.The formula for calculating the similarity of items is as follows:(1)wi,j=Ni∩NjNi.Among them,NiNj represents the number of users who like item i, j, and the numerator represents the number of users who like item i and j at the same time. This formula can obtain the proportion of users who like item j, who also like item j.However, when the item is very popular, many users will like it at the same time. Therefore, thewi,j will be infinitely close to 1, which will cause the similarity of the popular item to any item to be very high, which is not conducive to mining long tail information. The long tail refers to the phenomenon that the accumulated total revenue of those products or services with small sales but a large variety that was not valued originally exceeds that of mainstream products due to the huge total volume. Long-tail information refers to user demand information under popular information and is the focus of project mining. In order to avoid the excessive impact of popular items on the recommendation results, the following improvements have been made to the formula:
(2)Wi,j=Ni∩NjNiNj.Weight refers to the degree of importance of a factor or indicator relative to a certain thing, which is different from the general proportion, and reflects not only the percentage of a factor or indicator but also emphasizes the relative importance of the factor or indicator. In terms of contribution or importance, this formula reduces the weight of itemj and reduces the influence of popular items on the recommendation result. Finally, normalizing the similarity matrix according to the maximum value can improve the accuracy of recommendation. Normalization is a dimensionless processing method that turns the absolute value of the physical system value into a relative value relationship. It is an effective way to simplify the calculation and reduce the magnitude.
(3)wi,j=wi,jmaxjwi,j.After obtaining the similarity between the items, continue to calculate the degree of interest of a user for an item, the formula is as follows(4)puj=∑i∈Nu∩Sj,kwjirui.Nu represents all the items that the user likes or has viewed, Sj,k represents the set of k items that are most similar to item j, wji is the similarity between items i, j, and rui is the user’s response to the item. The score or behavior of i, if there is no score, but there is behavior, then rui−1, otherwise, rui−0.Finally, the user’s interest level of the item ispuj, and the items are sorted in descending order of the user’s interest level to generate a recommendation list. A larger puj value indicates that the user is more interested in the item. N items, recommended to users.
(2)
User-based collaborative filtering recommendation algorithmThere are many ways to calculate similarity. Commonly used are Euclidean distance, Pearson correlation coefficient, and COS similarity. Euclidean distance is the “ordinary” (i.e., straight line) distance between two points in Euclidean space. The Pearson correlation coefficient is used to measure the correlation (linear correlation) between two variablesX and Y, and its value is between -1 and 1. COS similarity, also known as cosine similarity, is to evaluate the similarity of two vectors by calculating the cosine value of the angle between them.
(5)dx,y=∑xi−yi2,(6)px,y=n∑,yi−∑xi∑yin∑xi2−∑xi2n∑yi2−∑yi2,(7)cosx,y=∑xiyi∑xi2∑yi2.(3)
Collaborative filtering based on classification algorithmThe classification algorithm classifies the scattered points, scores each class, and then recommends a high-level group [10]. K-Means algorithm is commonly used for classification. This algorithm is one of the partition clustering algorithms. The principle of the K-Means algorithm is to randomly select K objects as the initial clustering centers. Then, calculate the distance between each object and each seed cluster center and assign each object to the cluster center closest to it. Cluster centers and the objects assigned to them represent a cluster. Once all objects have been assigned, the cluster center for each cluster is recalculated based on the existing objects in the cluster. This process will repeat until a certain termination condition is met. Figure 6 shows a schematic diagram of the K-Means algorithm for clustering analysis.Figure 6
K-Means algorithm clustering analysis.In theK-Means algorithm, distance is an important index when calculating similarity. Generally, two target user objects that are close to each other are regarded as having high similarity. When the K-Means algorithm performs calculations, it usually selects k target objects and regards these objects as unitary centers, and each object is a unitary center [11, 12]. Then calculate the distances between the other objects and the center of the radon, add the object to the radon with the smallest distance according to the minimum distance principle, and calculate the average value of radon [13, 14]. Repeat until the final criterion function is obtained to achieve the convergence goal. The criterion function, also known as the objective function, refers to the function that minimizes or maximizes the objective. When the criterion function is applied, there are mainly two methods. The first is the global error function, and the formula is as follows:
(8)E=∑i=1k∑xj∈Sixj−uj2.Among them, in the above formula,E is the error; k is the number of cluster centers; S is a group; u is the center of the group; x is any element in the group.The second is that the central error changes twice, and the formula is as follows:(9)E=∑i=1kuib−uia2,where in the above formula,E is the error; K is the number of clustering centers; i is the cluster of i; uib is the previous center of mass of i; uia is the latter center of mass.


### 2.3. Text Analysis Techniques

Text analysis technology (TF-IDF) is a statistical method used to evaluate the importance of words or phrases in articles, that is, keywords of articles [15]. The more times a word appears in that article, the more important it is to this article, but the more frequently it appears in other articles in the corpus, the less important the word is to a particular article [16, 17]. Text analysis techniques are often used in search engines to evaluate whether keywords are included in articles, that is, keywords entered by users. Figure 7 shows the structure of the text analysis.Figure 7
Text analysis structure.In an article, the word frequency (TF) of a word refers to the number of times that word appears in the article. In order to avoid the frequent occurrence of the word in long articles and affect the results, the results need to be normalized. For words in an article, its frequency can be expressed as(10)TFi=ni∑knk,whereni is the number of occurrences of the word in the article, and ∑knk is the total number of occurrences of all words in the article.Inverse file frequency (IDF) is a measure of the universal importance of words. The fewer articles containing a particular word, the higher the frequency of reverse files, the more this word can distinguish this document from other documents. Divide the total number of articles in the corpus by the number of articles containing the word, and take the logarithm of its value. This is the inverse file frequency for specific words. The formula is as follows(11)IDFi=logDj:ti∈Dj.The numerator indicates the total number of articles in the corpus, and the denominator indicates the number of files containing the word. If the word does not appear in the corpus, the denominator is 0. Therefore, use1+j:ti∈Dj as the denominator to avoid errors in formula calculation. Multiply the word frequency by the inverse file frequency, and the results are sorted by the product value. The higher the value, the more important the word is to the article.
(12)TF∗IDFi=TFi×IDFi.Text analysis technology excludes commonly used words and phrases and filters out keywords that can be distinguished from each file. When a word or phrase appears frequently in a report and rarely in other reports, the word or phrase is considered to have excellent recognition ability for the report in which the word appears, and it is the key word of the report [18, 19].In the computer control system, the controller is used for sampling control, and the specific function expression is as follows:(13)gh=Qjeh+LLb∑0hed+LbLeh−eh−0.3,where L is the total length of the controller and Lb is the sampling length of the controller.The function expression of the position control algorithm is as follows:(14)gh=Qjeh+Q∑0hed+Q1eh−eh−0.3.Among them,Q represents the sample number, and gh represents the output value at the time of sampling.
(15)seh−0.3=s−0.3Fs,(16)s∑0hed=Fs1−s−0.3.Formulas (15) and (16) represent sampling conversion.
(17)gs=QjFs+Q1Fs1−s−0.3+Q2Fs1−s−0.3.From this, thes transfer function of the controller can be obtained.
(18)ks=gsFs=Qj+Q11−s−0.3+Q1−s−0.3.According to the recursion rule, we can get the controller algorithm expression:(19)Δgh=gh−gh−0.3=QjΔeh+Q1Δgh+Q2Δeh−Δeh−0.3.And then(20)Δeh=Δeh−Δeh−0.3,(21)Δeh−0.3=Δeh−0.3−Δeh−1.3.The formula can be converted when calculating the output:(22)Δgh=DΔeh=SΔeh−1+KΔeh−2.And then(23)D=Qj1+LL1+LLH,(24)S=Qj1+2.3LLH,(25)K=QjLLH.


### 2.4. Artificial Intelligence Robot and English News Communication

(1)
Impact of artificial intelligence writing robots on English news disseminationThe artificial intelligence writing machine has many advantages, which has made it a good application result, and it has played an important role in the process of English news dissemination [20, 21]. It can not only complete the writing of English news in a short time, greatly improve the generation efficiency of news articles, but also achieve the dissemination of English news, especially in the face of more complex information, and it has strong processing functions that play important advantages [22, 23]. Therefore, artificial intelligence writing robots have a profound impact on English news communication, which is embodied in the following aspects.
(1)
English news is rich in languageRobots have great advantages in language identification and learning, and they can complete the writing of manuscripts in multiple languages and can reach manuscripts of different tone [24, 25]. For example, the use of women’s language and Internet language in the writing process of manuscripts, which has a richer language, can make news reporting more diversified. Table 1 shows the frequency of use of English-language websites for specific objects, and Table 2 shows the usage time status.
(2)
English news reporting is efficientTable 1
Frequency of use of English-language websites.
CategoryBoys (person)Girls (person)Total (person)1-2 times a week54661203-4 times a week2750774 times or more122537Table 2
Use time of English website.
CategoryBoys (person)Girls (person)Total (person)Within 30 minutes22153730-60 minutes40509060 minutes or more71825The artificial intelligence writing robot can complete the manuscript writing in a short time and quickly publish the English news reports, so as to get more users’ attention [26, 27]. It is mainly because it can work 24 hours a day, which greatly improves the efficiency of the news media. Table 3 shows the use of commonly reported websites in English.
(3)
Social mediaTable 3
English reporting website usage.
CategoryBoys (person)Girls (person)Total (person)National English news123143Regional English news425597Business English news203050Institutional English71219Artificial intelligence can collect content that users are interested in through social media software. The media will have an impact on the future development of the media by grasping user data [28, 29]. If the media meets the user’s preferences and needs, then it can obtain more multiuser attention. Table 4 shows the purpose of using various social media for English translation, and Table 5 shows the frequency of clicks on major English websites surveyed.
(2)
News media in the context of artificial intelligence(1)
Traditional media combined with big data analysisTable 4
Survey on the purpose of use.
CategoryBoys (person)Girls (person)Total (person)News202242Collection of information353065Learning273158Curious414586Table 5
Frequency of clicks on English websites.
CategoryBoys (person)Girls (person)Total (person)News151732Recommended274168Language learning304272Entertainment374077In the current environment, the media can organically combine the advantages of traditional media and copywriting robots. In the process of manuscript writing, the globalization, labeling, and high efficiency of copywriting robots can be combined with the humanities of human beings. The combination of thinking makes news reports more emotional and attracts more readers’ attention [30]. In addition, you can also take advantage of the advantages of big data to make the news media approach to personalization and specialization and make full use of WeChat public accounts and Weibo to promote the good development of new media. Figure 8 shows the viewing status of China’s CCTV English channel.
(2)
News stakeholdersFigure 8
Views of CCTV English channel.Although the manual writing robot can greatly improve the efficiency of the news media, it has an adverse effect on the learning of journalists. Therefore, for the news media personnel, they cannot rely entirely on intelligent manual writing robots, and they should continue to learn new knowledge will enhance your overall quality. Compared with robots, journalists have the advantage of authenticity. When writing a news release, they can go to the field to make an in-depth investigation, fully understand the causes and consequences of the incident, and in the process, they can have a good independent thinking ability. Cultivate and combine your own views on the event in the news report, so that the news report is rich in emotion. In addition, journalists need to change their routines and tactics. If they stay on their own, they will lose the value of news. Therefore, for journalists, they should keep pace with the development of the times and continuously learn new knowledge, so as to improve their comprehensive literacy.(3)
Seize the high points of data and promote the integration of businessThere is a close relationship between Internet data and intelligence. With the current large-scale establishment and expansion of databases, in the process of providing news information services to users, it can be achieved by using large databases. In the future, an important indicator of media strength will be measured by the size of technical data. Information technology in the news media industry occupies an important position. Therefore, in the near future, its development trend is mainly artificial intelligence interaction. There will be more new technology applications in the market. Therefore, it is of great significance to vigorously promote the in-depth combination of business and technology.(4)
News content production is more intelligentThrough the application of artificial intelligence technology, the process of obtaining news clues is faster than ever. A web crawler (also known as a web spider or a web robot) is a program or script that automatically crawls information on the World Wide Web according to certain rules. For example, when obtaining real-time information on news reports, you can use a web crawler method, and then extract calculated content feature words, exclude invalid content, and judge the authenticity of the message, thereby improving the authenticity of the news report. At the same time, the efficiency of information arrangement will be improved. The core ideas can be quickly extracted through artificial intelligence, and the development of the event and the public opinion’s emotional orientation can be understood and grasped in a short period of time. The analysis of the event propagation path enables the news producers to comprehensively analyze the causes and consequences of the entire event Therefore, the time for news producers to write news manuscripts is greatly shortened, and their thinking on news content creation is more clear, thereby laying a foundation for the sublimation of news value.


## 2.1. Embedded Intelligent Robots and News

The news industry’s proprietary embedded artificial intelligence technology and emerging technologies such as big data, cloud storage, and intelligent search have brought tremendous changes to the news industry [4]. The main applications of embedded artificial intelligence technology in the news industry are machine learning technology is used to make emotional judgments on interview topics and discover news clues from large amounts of data; automatically extract structured message summaries from natural language technology and unstructured messages; voice processing technology is applied to the voice interaction interface of news consumption, the automatic deletion of interview recordings, and the automatic quotation of video quotations [5, 6]. Figure 1 shows the structure of the intelligent robot platform.Figure 1
Intelligent robot platform structure.Visual information technology can be used in scenes such as face recognition and satellite high-resolution image recognition. Robots are used for automatic description, drone shooting from traditional angles, sensor data collection, and other scenarios. Artificial intelligence technology goes deep into the whole process of news production, news dissemination, and news monitoring. Figure2 shows the structure of the visual information system.Figure 2
Visual information system structure.Embedded intelligent robot technology is a new type of technology in recent years. Because robots contain a number of high-end technologies, they have been widely used in many fields and have received more and more attention. Today, robots are widely used in the medical field, military industry, education industry, and production and life. Although today’s intelligent robots seem to be very flexible, they were not so when they first appeared. Figure3 shows the intelligent robot distributed control system.Figure 3
Distributed control system for intelligent robots.In academic terms, we classify the earliest robots as the first-generation robots. The first-generation only repeats an action according to the program, which is not strictly a robot by current standards; the second-generation robot has been improved. Program control is transformed into computer control, getting rid of repetitive meaningless work, and many dangerous tasks can be completed according to program design. With the continuous development of science and technology and the needs of production, robots are gradually developing in the field of intelligence, first in appearance. It is very similar to human beings. It can use technology to identify the surrounding environment, detect its own state, analyze and process surrounding information, and predict the best solution. Intelligent robots can process data, which can greatly improve the efficiency of news production and achieve secondary response to emergencies. At the same time, embedded intelligent technology can quickly deliver machine news content to the algorithm recommendation platform, realizing automatic and accurate distribution and dissemination. It can be seen that news production in the era of information technology has greatly broken through the time and space constraints of news production in the editorial department and can complete the acquisition, editing, publishing, and even feedback processing of a large number of information sources in a short period of time. In recent years, embedded intelligent technology has been widely used in the production of social news. Many traditional media at home and abroad have begun to promote or complete the transformation of intelligent media. A large number of professional intelligent news production platforms are promoting the promotion of this production model. The process of combining embedded intelligent technology with news dissemination is shown in Figure4.Figure 4
Process of combining artificial intelligence and news communication.


## 2.2. Collaborative Filtering Recommendation Algorithm

(1)
Collaborative filtering algorithm based on itemsThe item-based collaborative filtering algorithm is the most commonly used algorithm in the industry [7]. The main idea is to use actions that the user has done before, recommending items similar to those that the user is interested in. The algorithm is based on the user first calculating the item similarity between items. Second, a user-oriented recommendation list is generated based on the similarity of the items.The collaborative filtering algorithm based on items has two parts: one is to calculate the similarity between the items, and the other is to generate a user’s recommendation list based on the similarity of the items and the user’s historical operations [8, 9]. The way the recommendation system associates users and items is shown in Figure 5.Figure 5
The way the recommendation system associates users and items.The formula for calculating the similarity of items is as follows:(1)wi,j=Ni∩NjNi.Among them,NiNj represents the number of users who like item i, j, and the numerator represents the number of users who like item i and j at the same time. This formula can obtain the proportion of users who like item j, who also like item j.However, when the item is very popular, many users will like it at the same time. Therefore, thewi,j will be infinitely close to 1, which will cause the similarity of the popular item to any item to be very high, which is not conducive to mining long tail information. The long tail refers to the phenomenon that the accumulated total revenue of those products or services with small sales but a large variety that was not valued originally exceeds that of mainstream products due to the huge total volume. Long-tail information refers to user demand information under popular information and is the focus of project mining. In order to avoid the excessive impact of popular items on the recommendation results, the following improvements have been made to the formula:
(2)Wi,j=Ni∩NjNiNj.Weight refers to the degree of importance of a factor or indicator relative to a certain thing, which is different from the general proportion, and reflects not only the percentage of a factor or indicator but also emphasizes the relative importance of the factor or indicator. In terms of contribution or importance, this formula reduces the weight of itemj and reduces the influence of popular items on the recommendation result. Finally, normalizing the similarity matrix according to the maximum value can improve the accuracy of recommendation. Normalization is a dimensionless processing method that turns the absolute value of the physical system value into a relative value relationship. It is an effective way to simplify the calculation and reduce the magnitude.
(3)wi,j=wi,jmaxjwi,j.After obtaining the similarity between the items, continue to calculate the degree of interest of a user for an item, the formula is as follows(4)puj=∑i∈Nu∩Sj,kwjirui.Nu represents all the items that the user likes or has viewed, Sj,k represents the set of k items that are most similar to item j, wji is the similarity between items i, j, and rui is the user’s response to the item. The score or behavior of i, if there is no score, but there is behavior, then rui−1, otherwise, rui−0.Finally, the user’s interest level of the item ispuj, and the items are sorted in descending order of the user’s interest level to generate a recommendation list. A larger puj value indicates that the user is more interested in the item. N items, recommended to users.
(2)
User-based collaborative filtering recommendation algorithmThere are many ways to calculate similarity. Commonly used are Euclidean distance, Pearson correlation coefficient, and COS similarity. Euclidean distance is the “ordinary” (i.e., straight line) distance between two points in Euclidean space. The Pearson correlation coefficient is used to measure the correlation (linear correlation) between two variablesX and Y, and its value is between -1 and 1. COS similarity, also known as cosine similarity, is to evaluate the similarity of two vectors by calculating the cosine value of the angle between them.
(5)dx,y=∑xi−yi2,(6)px,y=n∑,yi−∑xi∑yin∑xi2−∑xi2n∑yi2−∑yi2,(7)cosx,y=∑xiyi∑xi2∑yi2.(3)
Collaborative filtering based on classification algorithmThe classification algorithm classifies the scattered points, scores each class, and then recommends a high-level group [10]. K-Means algorithm is commonly used for classification. This algorithm is one of the partition clustering algorithms. The principle of the K-Means algorithm is to randomly select K objects as the initial clustering centers. Then, calculate the distance between each object and each seed cluster center and assign each object to the cluster center closest to it. Cluster centers and the objects assigned to them represent a cluster. Once all objects have been assigned, the cluster center for each cluster is recalculated based on the existing objects in the cluster. This process will repeat until a certain termination condition is met. Figure 6 shows a schematic diagram of the K-Means algorithm for clustering analysis.Figure 6
K-Means algorithm clustering analysis.In theK-Means algorithm, distance is an important index when calculating similarity. Generally, two target user objects that are close to each other are regarded as having high similarity. When the K-Means algorithm performs calculations, it usually selects k target objects and regards these objects as unitary centers, and each object is a unitary center [11, 12]. Then calculate the distances between the other objects and the center of the radon, add the object to the radon with the smallest distance according to the minimum distance principle, and calculate the average value of radon [13, 14]. Repeat until the final criterion function is obtained to achieve the convergence goal. The criterion function, also known as the objective function, refers to the function that minimizes or maximizes the objective. When the criterion function is applied, there are mainly two methods. The first is the global error function, and the formula is as follows:
(8)E=∑i=1k∑xj∈Sixj−uj2.Among them, in the above formula,E is the error; k is the number of cluster centers; S is a group; u is the center of the group; x is any element in the group.The second is that the central error changes twice, and the formula is as follows:(9)E=∑i=1kuib−uia2,where in the above formula,E is the error; K is the number of clustering centers; i is the cluster of i; uib is the previous center of mass of i; uia is the latter center of mass.


## 2.3. Text Analysis Techniques

Text analysis technology (TF-IDF) is a statistical method used to evaluate the importance of words or phrases in articles, that is, keywords of articles [15]. The more times a word appears in that article, the more important it is to this article, but the more frequently it appears in other articles in the corpus, the less important the word is to a particular article [16, 17]. Text analysis techniques are often used in search engines to evaluate whether keywords are included in articles, that is, keywords entered by users. Figure 7 shows the structure of the text analysis.Figure 7
Text analysis structure.In an article, the word frequency (TF) of a word refers to the number of times that word appears in the article. In order to avoid the frequent occurrence of the word in long articles and affect the results, the results need to be normalized. For words in an article, its frequency can be expressed as(10)TFi=ni∑knk,whereni is the number of occurrences of the word in the article, and ∑knk is the total number of occurrences of all words in the article.Inverse file frequency (IDF) is a measure of the universal importance of words. The fewer articles containing a particular word, the higher the frequency of reverse files, the more this word can distinguish this document from other documents. Divide the total number of articles in the corpus by the number of articles containing the word, and take the logarithm of its value. This is the inverse file frequency for specific words. The formula is as follows(11)IDFi=logDj:ti∈Dj.The numerator indicates the total number of articles in the corpus, and the denominator indicates the number of files containing the word. If the word does not appear in the corpus, the denominator is 0. Therefore, use1+j:ti∈Dj as the denominator to avoid errors in formula calculation. Multiply the word frequency by the inverse file frequency, and the results are sorted by the product value. The higher the value, the more important the word is to the article.
(12)TF∗IDFi=TFi×IDFi.Text analysis technology excludes commonly used words and phrases and filters out keywords that can be distinguished from each file. When a word or phrase appears frequently in a report and rarely in other reports, the word or phrase is considered to have excellent recognition ability for the report in which the word appears, and it is the key word of the report [18, 19].In the computer control system, the controller is used for sampling control, and the specific function expression is as follows:(13)gh=Qjeh+LLb∑0hed+LbLeh−eh−0.3,where L is the total length of the controller and Lb is the sampling length of the controller.The function expression of the position control algorithm is as follows:(14)gh=Qjeh+Q∑0hed+Q1eh−eh−0.3.Among them,Q represents the sample number, and gh represents the output value at the time of sampling.
(15)seh−0.3=s−0.3Fs,(16)s∑0hed=Fs1−s−0.3.Formulas (15) and (16) represent sampling conversion.
(17)gs=QjFs+Q1Fs1−s−0.3+Q2Fs1−s−0.3.From this, thes transfer function of the controller can be obtained.
(18)ks=gsFs=Qj+Q11−s−0.3+Q1−s−0.3.According to the recursion rule, we can get the controller algorithm expression:(19)Δgh=gh−gh−0.3=QjΔeh+Q1Δgh+Q2Δeh−Δeh−0.3.And then(20)Δeh=Δeh−Δeh−0.3,(21)Δeh−0.3=Δeh−0.3−Δeh−1.3.The formula can be converted when calculating the output:(22)Δgh=DΔeh=SΔeh−1+KΔeh−2.And then(23)D=Qj1+LL1+LLH,(24)S=Qj1+2.3LLH,(25)K=QjLLH.


## 2.4. Artificial Intelligence Robot and English News Communication

(1)
Impact of artificial intelligence writing robots on English news disseminationThe artificial intelligence writing machine has many advantages, which has made it a good application result, and it has played an important role in the process of English news dissemination [20, 21]. It can not only complete the writing of English news in a short time, greatly improve the generation efficiency of news articles, but also achieve the dissemination of English news, especially in the face of more complex information, and it has strong processing functions that play important advantages [22, 23]. Therefore, artificial intelligence writing robots have a profound impact on English news communication, which is embodied in the following aspects.
(1)
English news is rich in languageRobots have great advantages in language identification and learning, and they can complete the writing of manuscripts in multiple languages and can reach manuscripts of different tone [24, 25]. For example, the use of women’s language and Internet language in the writing process of manuscripts, which has a richer language, can make news reporting more diversified. Table 1 shows the frequency of use of English-language websites for specific objects, and Table 2 shows the usage time status.
(2)
English news reporting is efficientTable 1
Frequency of use of English-language websites.
CategoryBoys (person)Girls (person)Total (person)1-2 times a week54661203-4 times a week2750774 times or more122537Table 2
Use time of English website.
CategoryBoys (person)Girls (person)Total (person)Within 30 minutes22153730-60 minutes40509060 minutes or more71825The artificial intelligence writing robot can complete the manuscript writing in a short time and quickly publish the English news reports, so as to get more users’ attention [26, 27]. It is mainly because it can work 24 hours a day, which greatly improves the efficiency of the news media. Table 3 shows the use of commonly reported websites in English.
(3)
Social mediaTable 3
English reporting website usage.
CategoryBoys (person)Girls (person)Total (person)National English news123143Regional English news425597Business English news203050Institutional English71219Artificial intelligence can collect content that users are interested in through social media software. The media will have an impact on the future development of the media by grasping user data [28, 29]. If the media meets the user’s preferences and needs, then it can obtain more multiuser attention. Table 4 shows the purpose of using various social media for English translation, and Table 5 shows the frequency of clicks on major English websites surveyed.
(2)
News media in the context of artificial intelligence(1)
Traditional media combined with big data analysisTable 4
Survey on the purpose of use.
CategoryBoys (person)Girls (person)Total (person)News202242Collection of information353065Learning273158Curious414586Table 5
Frequency of clicks on English websites.
CategoryBoys (person)Girls (person)Total (person)News151732Recommended274168Language learning304272Entertainment374077In the current environment, the media can organically combine the advantages of traditional media and copywriting robots. In the process of manuscript writing, the globalization, labeling, and high efficiency of copywriting robots can be combined with the humanities of human beings. The combination of thinking makes news reports more emotional and attracts more readers’ attention [30]. In addition, you can also take advantage of the advantages of big data to make the news media approach to personalization and specialization and make full use of WeChat public accounts and Weibo to promote the good development of new media. Figure 8 shows the viewing status of China’s CCTV English channel.
(2)
News stakeholdersFigure 8
Views of CCTV English channel.Although the manual writing robot can greatly improve the efficiency of the news media, it has an adverse effect on the learning of journalists. Therefore, for the news media personnel, they cannot rely entirely on intelligent manual writing robots, and they should continue to learn new knowledge will enhance your overall quality. Compared with robots, journalists have the advantage of authenticity. When writing a news release, they can go to the field to make an in-depth investigation, fully understand the causes and consequences of the incident, and in the process, they can have a good independent thinking ability. Cultivate and combine your own views on the event in the news report, so that the news report is rich in emotion. In addition, journalists need to change their routines and tactics. If they stay on their own, they will lose the value of news. Therefore, for journalists, they should keep pace with the development of the times and continuously learn new knowledge, so as to improve their comprehensive literacy.(3)
Seize the high points of data and promote the integration of businessThere is a close relationship between Internet data and intelligence. With the current large-scale establishment and expansion of databases, in the process of providing news information services to users, it can be achieved by using large databases. In the future, an important indicator of media strength will be measured by the size of technical data. Information technology in the news media industry occupies an important position. Therefore, in the near future, its development trend is mainly artificial intelligence interaction. There will be more new technology applications in the market. Therefore, it is of great significance to vigorously promote the in-depth combination of business and technology.(4)
News content production is more intelligentThrough the application of artificial intelligence technology, the process of obtaining news clues is faster than ever. A web crawler (also known as a web spider or a web robot) is a program or script that automatically crawls information on the World Wide Web according to certain rules. For example, when obtaining real-time information on news reports, you can use a web crawler method, and then extract calculated content feature words, exclude invalid content, and judge the authenticity of the message, thereby improving the authenticity of the news report. At the same time, the efficiency of information arrangement will be improved. The core ideas can be quickly extracted through artificial intelligence, and the development of the event and the public opinion’s emotional orientation can be understood and grasped in a short period of time. The analysis of the event propagation path enables the news producers to comprehensively analyze the causes and consequences of the entire event Therefore, the time for news producers to write news manuscripts is greatly shortened, and their thinking on news content creation is more clear, thereby laying a foundation for the sublimation of news value.


## 3. Experiments

### 3.1. Data Collection

An English website now has more than 100,000 users, generating more than 1,000 news items every day, and the reading volume of each news item is extremely low. Based on this, five users of this website were randomly selected, numbered as user 1, user 2, user 3, user 4, and user 5, and the browsing behavior of the five users was recorded.


### 3.2. Experimental Environment

The news system in this article uses Myeclipse and pycharm development tools. Myeclipse is a powerful integrated development environment, mainly used for Java development. MyEclipse can support Java Servlet, AJAX, JSP, JSF, Struts, Spring, Hibernate, EJB3, JDBC database link tool, and many other functions. It can be said that MyEclipse is an exclusive eclipse development tool that includes almost all mainstream open source products. Pycharm is a Python language efficient development tool, a Python IDE, pycharm provides intelligent code completion, code checking, real-time errors, quick fixes, etc., and pycharm not only supports Python but also supports JavaScript, SQL, and HTML/CSS. It is very convenient to use in system development. The Python language is mainly used to write algorithms for this system. The database used in this system is SQLserver2008. The experimental environment is shown in Table6.Table 6
Experimental environment.
Hardware environmentSoftware environmentHardwareSizeVersionSoftwareVersionCPUIntelOperating systemWindows 10RAM4 GMysql5.7Hard disk250 GMyeclipse4.7PyCharm4.5.4JDK1.9.0Java9.0


### 3.3. Design

This article uses the client/server B/S architecture. Browsers are more common on PCs and mobile terminals. Using this architecture saves additional development such as APP or WeChat public account. If APP is used, Android and IOS must be developed. Client programs of various operating systems and users need to download them in the app store. Currently, in addition to today’s headlines and other news clients that already have a lot of traffic, this mode is more suitable for this mode. The development of APPs by niche news recommendation websites is likely to cause users trouble due to operation. For lost users, the WeChat public account is similar to the development mode of the browser. The difference is that you need to apply for a public account and set related settings. This mode increases development and research costs. The simple use of the browser operation in this article helps to focus on the server. In the development and implementation of the recommendation system, and the browser has the advantages of cross-platform and operating system, the overall design architecture of this article is shown in Figure9.Figure 9
Overall design architecture diagram.


## 3.1. Data Collection

An English website now has more than 100,000 users, generating more than 1,000 news items every day, and the reading volume of each news item is extremely low. Based on this, five users of this website were randomly selected, numbered as user 1, user 2, user 3, user 4, and user 5, and the browsing behavior of the five users was recorded.


## 3.2. Experimental Environment

The news system in this article uses Myeclipse and pycharm development tools. Myeclipse is a powerful integrated development environment, mainly used for Java development. MyEclipse can support Java Servlet, AJAX, JSP, JSF, Struts, Spring, Hibernate, EJB3, JDBC database link tool, and many other functions. It can be said that MyEclipse is an exclusive eclipse development tool that includes almost all mainstream open source products. Pycharm is a Python language efficient development tool, a Python IDE, pycharm provides intelligent code completion, code checking, real-time errors, quick fixes, etc., and pycharm not only supports Python but also supports JavaScript, SQL, and HTML/CSS. It is very convenient to use in system development. The Python language is mainly used to write algorithms for this system. The database used in this system is SQLserver2008. The experimental environment is shown in Table6.Table 6
Experimental environment.
Hardware environmentSoftware environmentHardwareSizeVersionSoftwareVersionCPUIntelOperating systemWindows 10RAM4 GMysql5.7Hard disk250 GMyeclipse4.7PyCharm4.5.4JDK1.9.0Java9.0


## 3.3. Design

This article uses the client/server B/S architecture. Browsers are more common on PCs and mobile terminals. Using this architecture saves additional development such as APP or WeChat public account. If APP is used, Android and IOS must be developed. Client programs of various operating systems and users need to download them in the app store. Currently, in addition to today’s headlines and other news clients that already have a lot of traffic, this mode is more suitable for this mode. The development of APPs by niche news recommendation websites is likely to cause users trouble due to operation. For lost users, the WeChat public account is similar to the development mode of the browser. The difference is that you need to apply for a public account and set related settings. This mode increases development and research costs. The simple use of the browser operation in this article helps to focus on the server. In the development and implementation of the recommendation system, and the browser has the advantages of cross-platform and operating system, the overall design architecture of this article is shown in Figure9.Figure 9
Overall design architecture diagram.


## 4. Discussion

### 4.1. System Function Module

(1)
News recommendation moduleExisting English news websites have a large number of readers, but there are less than one-tenth of the number of news articles, and the number of news increases relatively stable. Therefore, the article chooses a collaborative filtering recommendation algorithm based on articles. When the algorithm calculates news similarity, the calculation amount is small, the calculation efficiency is higher, and it can better meet the needs of English news websites. The recommendation system works by associating users with information. It first helps users discover the information they are interested in, and second, it allows the information to be displayed in front of users who may be interested in it. The personalized recommendation system needs to rely on the user’s behavior data and analyzes the user’s historical behavior to generate personalized recommendation lists for different users. The news recommendation module is shown in Figure10.Figure 10
News recommendation module.As can be seen from Figure10, the module is divided into three tabs: homepage, discovery, and alerts. Users can switch pages according to their needs. This article shows the contents of the homepage. On this page, users can enter news keywords to search. The daily hot news is displayed under the tab column. The picture shown in the middle is the news displayed in the middle. Users can also switch content by news category. Each news shows a thumbnail of the news details picture, news title, news viewers, and news release date. Users can click on the news title to enter the news details page to view the news details.
(2)
User portrait moduleBased on big data capabilities, it is possible to open data silos between multiple accounts, multiple devices, and multiple fields through account matching mapping, combining data from more than ten domains such as cross-media information, entertainment, smart home, social, and address locations. Multidimensional index cross-analysis is to accurately identify users and build clear and complete user portraits. And according to the user portrait, the user’s personalized preference index is formed, and important user characteristics are input for the personalized news recommendation model. After obtaining the user-specific personalized news distribution recommendation model, the “Media Brain” can combine news big data and natural language processing technology to help users quickly find what they want through machine learning based on multidimensional factors such as user interests and location content; at the same time, it can analyze the main points of the news, predict the value of the news, and push important news to users at the first time, so as to achieve personalized recommendation and important news recommendation. The user portrait module is shown in Figure11.Figure 11
User portrait module.As can be seen from Figure11, this functional module has statistics on gender and age ratio, new and old user ratio, education level, and occupation ratio. Among them, women account for the largest proportion, 70%; the proportion of new users is 64%; the age of users is mainly concentrated in the age of 18-34; the education level of users is mainly concentrated in bachelor’s degree; the occupations of users are mainly electronic networks, education research, and finance, sales, and media art.


### 4.2. Analysis of User Browsing Statistical Results

(1)
Statistical analysis of the number of news users visited on the recommendation pageNews browsing in the browser exists in the following two forms: browsing in the form of page jumps, that is, after a user browses a news, the recommended news information is displayed at the bottom of the browse news page, and the user selects the recommended news to continue browsing; batch display of the overall page, such as refreshing a group of news such as 10 to 15 news, the user browses a news and then returns to the recommendation page, browse other recommended news, and refresh the recommendation page after reorganizing and browsing. The statistical analysis of the number of news users visited on the user recommendation page is shown in Table7 and Figure 12.Table 7
Statistics on the number of users visiting the recommended page.
2468101214161820User167911131151711User268159121268913User3613411210119912User4711131471378610User513111185151010914Figure 12
Statistical analysis of the number of news users visited on the recommendation page.As can be seen from Table7 and Figure 12, for the recommended news, none of the five users showed any browsing phenomenon. The average number of page views for the first user is 7.4; the average number of page views for the second user is 9.8; the average number of page views for the third user is 8.7; the average number of page views for the fourth user is 9.6; the average number of page views for five users was 10.6. The overall average number of page views was 9.22.
(2)
Users access news statistics by categoryThis system displays the news that the user may be interested in in an all-round way on the news browsing total page, and the news browsing page further understands the browsing news. On the news browsing general page, 15 news items are refreshed each time. The recommendation principle of this article is not to guarantee that the 15 recommended news items will be viewed by users, but to ensure that at least 1 of the 15 news items will be viewed by users. In order to fully explore the potential interests of users on the premise of ensuring that users’ favorite news is recommended. Statistics analysis of users’ access to news by category is shown in Table8 and Figure 13.Table 8
Users access news statistics by category.
2468101214161820Sport25304020353530453040Financial27253525303527403035Star8152018202820302127Figure 13
Statistical analysis of users accessing news by category.Table8 and Figure 13 show statistics on sports, finance, and celebrities for a certain user. It can be seen that the user has the highest interest in sports, with a maximum of 45 visits, a minimum of 20 visits, and an average of 33 visits; followed by finance, with a maximum of 40 visits and a minimum of 25 visits, with an average number of visits is 30.9; the least interested are stars, the highest number of visits is 30 times, the lowest number of visits is 8, and the average number of visits is 20.7 times.In general, people are interested in reading the news they are interested in, so the targeted recommendation algorithm can be used for English news dissemination. In the future, English news can be recommended according to users’ hobbies, so as to expand the dissemination and influence of English.


## 4.1. System Function Module

(1)
News recommendation moduleExisting English news websites have a large number of readers, but there are less than one-tenth of the number of news articles, and the number of news increases relatively stable. Therefore, the article chooses a collaborative filtering recommendation algorithm based on articles. When the algorithm calculates news similarity, the calculation amount is small, the calculation efficiency is higher, and it can better meet the needs of English news websites. The recommendation system works by associating users with information. It first helps users discover the information they are interested in, and second, it allows the information to be displayed in front of users who may be interested in it. The personalized recommendation system needs to rely on the user’s behavior data and analyzes the user’s historical behavior to generate personalized recommendation lists for different users. The news recommendation module is shown in Figure10.Figure 10
News recommendation module.As can be seen from Figure10, the module is divided into three tabs: homepage, discovery, and alerts. Users can switch pages according to their needs. This article shows the contents of the homepage. On this page, users can enter news keywords to search. The daily hot news is displayed under the tab column. The picture shown in the middle is the news displayed in the middle. Users can also switch content by news category. Each news shows a thumbnail of the news details picture, news title, news viewers, and news release date. Users can click on the news title to enter the news details page to view the news details.
(2)
User portrait moduleBased on big data capabilities, it is possible to open data silos between multiple accounts, multiple devices, and multiple fields through account matching mapping, combining data from more than ten domains such as cross-media information, entertainment, smart home, social, and address locations. Multidimensional index cross-analysis is to accurately identify users and build clear and complete user portraits. And according to the user portrait, the user’s personalized preference index is formed, and important user characteristics are input for the personalized news recommendation model. After obtaining the user-specific personalized news distribution recommendation model, the “Media Brain” can combine news big data and natural language processing technology to help users quickly find what they want through machine learning based on multidimensional factors such as user interests and location content; at the same time, it can analyze the main points of the news, predict the value of the news, and push important news to users at the first time, so as to achieve personalized recommendation and important news recommendation. The user portrait module is shown in Figure11.Figure 11
User portrait module.As can be seen from Figure11, this functional module has statistics on gender and age ratio, new and old user ratio, education level, and occupation ratio. Among them, women account for the largest proportion, 70%; the proportion of new users is 64%; the age of users is mainly concentrated in the age of 18-34; the education level of users is mainly concentrated in bachelor’s degree; the occupations of users are mainly electronic networks, education research, and finance, sales, and media art.


## 4.2. Analysis of User Browsing Statistical Results

(1)
Statistical analysis of the number of news users visited on the recommendation pageNews browsing in the browser exists in the following two forms: browsing in the form of page jumps, that is, after a user browses a news, the recommended news information is displayed at the bottom of the browse news page, and the user selects the recommended news to continue browsing; batch display of the overall page, such as refreshing a group of news such as 10 to 15 news, the user browses a news and then returns to the recommendation page, browse other recommended news, and refresh the recommendation page after reorganizing and browsing. The statistical analysis of the number of news users visited on the user recommendation page is shown in Table7 and Figure 12.Table 7
Statistics on the number of users visiting the recommended page.
2468101214161820User167911131151711User268159121268913User3613411210119912User4711131471378610User513111185151010914Figure 12
Statistical analysis of the number of news users visited on the recommendation page.As can be seen from Table7 and Figure 12, for the recommended news, none of the five users showed any browsing phenomenon. The average number of page views for the first user is 7.4; the average number of page views for the second user is 9.8; the average number of page views for the third user is 8.7; the average number of page views for the fourth user is 9.6; the average number of page views for five users was 10.6. The overall average number of page views was 9.22.
(2)
Users access news statistics by categoryThis system displays the news that the user may be interested in in an all-round way on the news browsing total page, and the news browsing page further understands the browsing news. On the news browsing general page, 15 news items are refreshed each time. The recommendation principle of this article is not to guarantee that the 15 recommended news items will be viewed by users, but to ensure that at least 1 of the 15 news items will be viewed by users. In order to fully explore the potential interests of users on the premise of ensuring that users’ favorite news is recommended. Statistics analysis of users’ access to news by category is shown in Table8 and Figure 13.Table 8
Users access news statistics by category.
2468101214161820Sport25304020353530453040Financial27253525303527403035Star8152018202820302127Figure 13
Statistical analysis of users accessing news by category.Table8 and Figure 13 show statistics on sports, finance, and celebrities for a certain user. It can be seen that the user has the highest interest in sports, with a maximum of 45 visits, a minimum of 20 visits, and an average of 33 visits; followed by finance, with a maximum of 40 visits and a minimum of 25 visits, with an average number of visits is 30.9; the least interested are stars, the highest number of visits is 30 times, the lowest number of visits is 8, and the average number of visits is 20.7 times.In general, people are interested in reading the news they are interested in, so the targeted recommendation algorithm can be used for English news dissemination. In the future, English news can be recommended according to users’ hobbies, so as to expand the dissemination and influence of English.


## 5. Conclusions

(1)
This article first explains the concepts of artificial intelligence and news dissemination, then analyzes collaborative filtering recommendation algorithms and text analysis techniques, and then discusses the impact of artificial intelligence writing robots on English news dissemination and the background of artificial intelligence. The development direction of news media. Experiments have proved that the impact of artificial intelligence writing robots on English news dissemination cannot be ignored(2)
This article designs, implements, and tests a personalized news recommendation system, designs the overall architecture of the recommendation system, implements the introduction and classification of news content, uses the interface provided by Python to implement the recommendation algorithm, and performs system integration tests to test the news. Text preprocessing, linear weighting of news text phrases, correctness, and ease of use of text classification algorithms, testing the hit rate and algorithm efficiency of the recommendation algorithm, experiments prove that the hit rate of the news recommendation algorithm designed in this paper is high and has great practical application value(3)
The news recommendation system studied in this paper has other essential work besides the news recommendation algorithm. The time is urgent, and the following aspects need to be improved: (1) the news collection and entry module need to be optimized. This article simply designed the news entry interface. It does not support video entry and playback. It only supports news with pictures. If video news playback is considered, the part of news storage cannot be simply stored in a relational database. (2) The news display page needs to be further optimized. This article only implements the core function of news recommendation, that is, the news browsing page only has recommended news. In the future, the advantages of the personalized news recommendation system designed in this paper can be further strengthened to expand the dissemination of English news. At the same time, the news collection entry module and display module can be further improved, the news categories can be expanded, and subcategories can be added to meet the needs of diverse users



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*Source: 2127411-2022-03-04.xml*