# Econometric Analysis of the Hot Research of Marxist Theoretical Journals Based on Knowledge Map

Authors: Lan Wang; Kai Wang
Journal: Discrete Dynamics in Nature and Society
 (2022)

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## Abstract

In recent years, the research on Marxist theory has received a lot of attention. Many studies use quantitative research methods such as statistical keywords and constructing knowledge map to carry out Marxist theoretical analysis. In this paper, by constructing a self-expanding Chinese word separation and self-expanding address data knowledge graph, matching Marxist Chinese addresses based on the full-text indexing knowledge graph, incorporating a weighted pinyin full-text search mechanism to improve the matching accuracy of misspelled addresses, constructing a multiple matching mechanism for Marxist addresses by combining an online geographic parsing interface, and performing semisupervised matching for a small number of difficult addresses, a complete system of Marxist address matching methods is formed. By studying and analyzing the basic orientation and attention to academic hotspots, we can gain insight into the characteristics, rules, and problems of academic hotspots in Marxist theory journals.


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## Body

## 1. Introduction

The term Ecology is derived from the Greek words by Oikos (dwelling or place of living) and Logos (discipline or theory) and is interpreted in its own sense as the science of the habitat of living organisms. This includes both the living and the nonliving environment. Ecology seeks to harmonize human beings with their natural environment. However, human beings depend on the natural environment and are individuals of the social environment. The information environment, consisting of information infrastructure, information resources, information technology, information culture, and information ethics, is an important part of the social environment, which leads to the study of people, social organizations, and the information environment, that is, information ecology [1].Information ecology is a brand-new research field bred from the integration of information science and ecology, aiming to promote the orderly operation of human and information environment, the balance of information ecosystem, and even the sustainable and healthy development of human society [2–4]. The research method of information ecology is also a forward-looking research and design method, which focuses on the macroscopic examination and analysis of the relationship between information, people, and information environment from the perspective of promoting and maintaining the balance of the whole information ecosystem from the system as a whole and carries out reasonable planning, layout, and regulation of the information ecosystem in order to realize the stability and order of the information ecology [5]. Based on the above explanation, thinking about the relationship between human and information environment from the perspective of ecology is not only important for the field of information management discipline but also has practical significance for its study of today’s information society [6].On the basis of quantitative bibliographic statistics, CiteSpace knowledge mapping software was used to summarize the hotspots of journal concerns in the discipline of Marxist theory. The evaluation of professional journals in the discipline of Marxist theory by four academic evaluation institutions, namely, Chinese Academy of Social Sciences, Nanjing University, Peking University, and Wuhan University, supplemented by the classification of professional journals in the discipline of Marxist theory by three journal databases, namely, China Knowledge Network, Wanfang, and Vipshop, was integrated to meet the requirements for the analysis of hotspots of attention of journals in the discipline of Marxist theory [7–9].


## 2. Research Methodology and Sample Selection

### 2.1. Principles of Coword Analysis

The object of bibliographic research is rooted in the voluminous literature of all kinds. It uses statistical and mathematical methods to quantitatively analyze the characteristics of knowledge carriers on the basis of the “quantitative” output of various types of literature, so as to study and reveal the laws of literature and intelligence, grasp the hot spots of literature research, and foresee the development trend of scientific fields. Bibliolatrous involves methods such as citation analysis, coauthorship analysis, and coword analysis [10]. Coterm analysis is a content analysis technique that analyzes the number of occurrences of a pair of words in the same document, and then clusters these words hierarchically to reveal their affinities and relationships, and then analyzes the structural changes of the disciplines and topics they represent [11–13].The key to the coword analysis method is the selection of representative words. Since title words, keywords, and subject words are often refined by authors or editors based on the main idea of the article and express the research theme of the field to which they belong, such words are used as the carrier of coword analysis. In addition, because the selected literature involves a large number of keywords and subject terms, the threshold value is often limited according to the frequency of key terms to ensure the representativeness of the field to which they belong [14]. By counting the frequency of high-frequency subject terms and keywords in literature, a word-part coword matrix consisting of subject terms and keywords is formed, which can be analyzed by clustering and multidimensional regression analysis with statistical analysis software and supplemented by visualization analysis software to graphically express the research hotspots-visualization mapping [15–17].


### 2.2. Selection of Sample Data

The common word analysis is based on the extraction of high-frequency keywords from the literature base. In the Chinese journal full-text database (CNKI), the exact search of journal literature using “information ecology” as the subject term is voluminous, and the selection of all journal literature is not worth the cost in terms of economic and feasibility [18]. On the one hand, CSSCI journals represent a certain academic influence, and their publications have academic influence; on the other hand, the purpose of this study is to trace and prospect hot topics, so the time interception of the literature is the latest decade. In order to ensure the reliability of the hot topics of “information ecology”, 6 nonacademic documents such as notices and news reports were deleted, and finally, 500 documents were selected as the research sample, and the information was listed in the “Document Management Center” of China Knowledge Network. We downloaded the title information of these documents into the local literature database [19–22].As shown in Figure1, Marxist theory journals focused heavily on the 18th Party Congress, a major political event. “Scientific outlook on development”, “Marxism Chineseization”, “General Secretary Hu Jintao”, “reform and opening up”, “common prosperity” and “common wealth”. The keywords “socialism with Chinese characteristics”, “scientific development outlook”, “Marxist Chineseization”, “General Secretary Hu Jintao”, “reform and opening up”, “common prosperity”, and “18th National Congress” are directly related to the 18th Party Congress held in this year. Other keywords such as “socialist core value system” and “ecological civilization construction” are also intrinsically related to the 18th Party Congress [23].Figure 1
Keyword mapping of Marxist theory journals.


## 2.1. Principles of Coword Analysis

The object of bibliographic research is rooted in the voluminous literature of all kinds. It uses statistical and mathematical methods to quantitatively analyze the characteristics of knowledge carriers on the basis of the “quantitative” output of various types of literature, so as to study and reveal the laws of literature and intelligence, grasp the hot spots of literature research, and foresee the development trend of scientific fields. Bibliolatrous involves methods such as citation analysis, coauthorship analysis, and coword analysis [10]. Coterm analysis is a content analysis technique that analyzes the number of occurrences of a pair of words in the same document, and then clusters these words hierarchically to reveal their affinities and relationships, and then analyzes the structural changes of the disciplines and topics they represent [11–13].The key to the coword analysis method is the selection of representative words. Since title words, keywords, and subject words are often refined by authors or editors based on the main idea of the article and express the research theme of the field to which they belong, such words are used as the carrier of coword analysis. In addition, because the selected literature involves a large number of keywords and subject terms, the threshold value is often limited according to the frequency of key terms to ensure the representativeness of the field to which they belong [14]. By counting the frequency of high-frequency subject terms and keywords in literature, a word-part coword matrix consisting of subject terms and keywords is formed, which can be analyzed by clustering and multidimensional regression analysis with statistical analysis software and supplemented by visualization analysis software to graphically express the research hotspots-visualization mapping [15–17].


## 2.2. Selection of Sample Data

The common word analysis is based on the extraction of high-frequency keywords from the literature base. In the Chinese journal full-text database (CNKI), the exact search of journal literature using “information ecology” as the subject term is voluminous, and the selection of all journal literature is not worth the cost in terms of economic and feasibility [18]. On the one hand, CSSCI journals represent a certain academic influence, and their publications have academic influence; on the other hand, the purpose of this study is to trace and prospect hot topics, so the time interception of the literature is the latest decade. In order to ensure the reliability of the hot topics of “information ecology”, 6 nonacademic documents such as notices and news reports were deleted, and finally, 500 documents were selected as the research sample, and the information was listed in the “Document Management Center” of China Knowledge Network. We downloaded the title information of these documents into the local literature database [19–22].As shown in Figure1, Marxist theory journals focused heavily on the 18th Party Congress, a major political event. “Scientific outlook on development”, “Marxism Chineseization”, “General Secretary Hu Jintao”, “reform and opening up”, “common prosperity” and “common wealth”. The keywords “socialism with Chinese characteristics”, “scientific development outlook”, “Marxist Chineseization”, “General Secretary Hu Jintao”, “reform and opening up”, “common prosperity”, and “18th National Congress” are directly related to the 18th Party Congress held in this year. Other keywords such as “socialist core value system” and “ecological civilization construction” are also intrinsically related to the 18th Party Congress [23].Figure 1
Keyword mapping of Marxist theory journals.


## 3. Matching Based on Self-Extending Knowledge Graphs for Full-Text Search

### 3.1. Semisupervised Splitting Method

Whether building Chinese full-text indexes or preprocessing source addresses, credible Chinese word separation methods are needed. In this paper, we combine the advantages of lexical word separation and statistical-based word separation methods to avoid a large amount of training and huge dictionary construction requirements by using statistical word separation models on the one hand and to expand the dictionary on a rolling basis based on correctly identified data and threshold judgment mechanism on the other hand. The basic dictionary consists of short name data of national townships and above, administrative regions and enterprise suffix words, and data tables of petrochemical enterprise information in Ningbo [24], and the structure of the dictionary is as follows:(1)vi=w,f,t,where i∈N, vi denote the i-th word, and w, f, and t denote the address short name, word frequency, and the word nature, respectively. When building the basic dictionary, the word frequency is set to 10 by default, and the subsequent successful word separation results and matches will be increased automatically by the program. Only three types of lexical properties are considered in the basic dictionary, namely, ns (place name), hm (enterprise, mainly chemical plant), su (suffix words, such as province, city, district, autonomous region, limited company, and other place name) [25].The prefix dictionary, TRIE dictionary tree, which is a type of hash lookup tree, is constructed before the word division to enable fast dictionary lookup. The DAG is stored in the form of a dictionary with the following structure:(2)Pi:na+nb,⋯,nx,where i,a,b,⋯,x∈N. Pi denotes the index of the i-th word in the input address and na denotes the end position of the a-th divisor of Pi as the prefix that has a word frequency greater than 0. Thus, the DAG records all possible cuts of the input address, and the next step is to find the path with maximum probability based on dynamic programming. The probability of each word is equal to the frequency of the word in the prefix divided by the sum of all word frequencies; if the word frequency is 0 or does not exist, it is set to 1. The probability of each word is calculated as follows:(3)pj=pj,a×pj,b×⋯×pj,x,where j,a,b,⋯,x∈N,pj denotes the probability of the jth cut or the jth path, and pj,a denotes the ath word frequency in the jth path. To facilitate the calculation, the above equation is taken logarithmically to obtain(4)logpj=logpj,a+logpj,b+⋯+logpj,x.In the Marxist address text, the semantic focus comes first, and the dynamic programming method is used to calculate the probabilities of all paths from front to back and select the path with the highest probability among them, that is, to obtain the better partitioning result applicable to the current dictionary [26, 27]. Since the basic lexicon already has more complete data, the above process can already get the correct word separation results for most of the input. In order to make up for the shortcomings of the basic dictionary, we set a minimum probability threshold of Pmin in this paper, so that the cut score result for any input address has(5)maxlogpj<Pmin.Then, the input address is recorded, the word is manually split, and the valid result is added to the dictionary and inserted at the end of the address to be split. If an invalid address is detected, the address is marked as invalid. Based on the calculation of the maximumlogpj for 1000 groups of words, Pmin is set to 80 based on a semisupervised intervention rate of 2%.


### 3.2. Full-Text Search Matching Score Mechanism

The full-text search matching score mechanism is built based on TF-IDF (term frequency-inverse document frequency) [28] technology to calculate the matching relevance of full-text search results, and the scoring function is calculated as follows:(6)sq,d=Nq.coordq,d.tftind.idft.normt,d,where q and d are the text of the query and the matching documents, and N(q) is a predefined normalized query statement, which usually has no effect on a particular query application. coordq,d indicates the number of query responses in a document, and the more query items appear in a document, the better the query matches the document, which is mainly used in a multicriteria query environment. In the address query, the successful query results can be used to build a perfect syllogism dictionary to convert a single query into multiple queries to improve the matching accuracy of q [29].idf denotes inverse document frequency (IDF), which is calculated as follows:(7)idfti=logDj:ti∈dj,where D is the number of documents and j:ti∈dj denotes the total number of words ti in the document dj. The fewer the occurrences of a word in a single document or more documents it appears in, the higher the relevance of the match, which can exclude the influence of various dummy words to some extent. In addition, this element indicates that a certain address gets a higher matching rate when it appears in more entities, so the later section will keep using the successfully matched addresses to extend the knowledge graph in the process of constructing the knowledge graph, so that the probability of partially matching the wrong address by chance decreases as the matching process proceeds.normt,d denotes the field length normalized value, which is related to the result of word separation and can generally be reduced to the reciprocal of the square root of the number of words, implying that the full-text search gives priority to matching long fields, so the long address fields will be established later in the process of building the knowledge graph to improve the success rate of address search and matching.


### 3.3. Knowledge Graph

Knowledge graphs are structured semantic knowledge bases that can well describe concepts and interrelationships in the physical world. The following are some of the new types of entities that are created in the updated knowledge graph: factory, chemical company, and others [30].According to the research of the full-text search for matching score mechanism, in order to improve the accuracy of address matching, the knowledge graph is constructed according to the fields as shown in Table1.Table 1
Knowledge graph field information table.
Corresponding phonetic fieldExplainNamePinyinAddress nameShort nameShort pinyinDesuffixIndex nameShort indexCustom index nameCode—Administrative region codeLocationLocation pinyinNormalized addressShort locationShort location pinyinDesuffix specification addressIn order to correctly parse the address misspellings in hazardous chemical manifest addresses, this paper establishes a field corresponding to the phonetic field with tone to obtain credible search results. In order to obtain more accurate address matching for full-text search, the knowledge graph is constructed by taking into account the influence of the full-text search for matching score calculation function on the full-text indexing results, and the detailed address field location is designed in this paper. The location field and short_location field enable the full-text address search to better avoid matching the wrong renamed addresses [31].The relationships between the knowledge graph entities include the dependent belong, which is expressed as(8)a−rel:belong−>b,where a and b denote entities, rel is a sign word indicating the relationship, belong indicates that the relationship is subordinate, and the arrow indicates the direction of the relationship. In the critical transport address analysis, the navigation distance relationship between cities is also involved:(9)a−rel:distance−b,where a,b∈city is the combination of all entities of type city. The distance relationship does not need to define the direction. Its value is the navigation distance between the city center coordinates calculated using the Gaode Map navigation API.After the basic knowledge graph is established, this paper constructs full-text indexes for Chinese and Pinyin based on the full-text search matching score mechanism described in Section2.2 for all entities in the knowledge graph, where the Chinese index uses the semisupervised word separation method described in Section 2.1, and the Pinyin index uses a simple space (unicode-whitespace) word separation method.


### 3.4. Full-Text Search Is Applied to Address Matching

There are generally two Chinese data items in the single address data of the electronic waybill for hazardous chemicals, one is the actual address and the other is the enterprise address. The actual address is more reliable information, while the enterprise address has limited reliability, and only when the actual address cannot be matched, the enterprise address is used for address search.The value of match score for full-text search results is calculated as follows:(10)score=waSa+1−waSc.Among them,Sa and Sc are the full-text search matching scores of the actual address and enterprise address, respectively; wa is the weight, and this paper mainly takes the matching result of the actual address as the basis, so the value is set to 0.8. Sa, Sc the calculation method is as follows:(11)S=Skanzi×whanzi+Spinyin×1−whanz.Skanzi and Spinyin are the matching score obtained by Chinese and Pinyin full-text search, respectively, and whanz is the weight of Chinese full-text search score, set to 0.8. If the full-text search returns empty results, the score is set to 0.Limited by multiple safety factors such as high-speed control and parking restrictions for dangerous goods, long-distance transportation of dangerous goods accounts for a relatively small percentage. The distance adjustment coefficient is added in the calculation of the matching degree value, which is calculated as follows:(12)Safter=Sbefore.θ.Among them,Safter and Sbefore are the full-text search matching scores before and after adjustment, respectively, and θ is the distance adjustment factor. The calculation is as follows:(13)Ci:li,1:si1,li,2:si,2,⋯,li,n:si,n,where Ci denotes the i-th city, li,n denotes the n-th full-text search result address belonging to Ci, and si,n denotes the full-text search matching score corresponding to li,n. Let(14)s¯=aversi,1,si,2,⋯,si,n,smax=maxsi,1,si,2,⋯,si,n.Letm denotes the number of si,1,si,2,⋯,si,ns greater than s¯, and then, the matching score of city Ci is calculated as follows:(15)SCi=smax·2mn.As can be seen from the above equation, the greater the number of matched results and the larger the score, the greater theSCi is and the higher the error tolerance for partial accidental mismatches. The final matching result is SCi.Ifsi,n≤3, then the match is considered invalid, the address is skipped, and the address is added to the geographic resolution interface of Baidu or Gaode Maps, and the search result is confirmed only if the results output by the two major service providers is consistent; otherwise, it is added to the address to be supervised for classification. If si,n>3, then the matching result is entered, the word frequency of the subword dictionary is updated, and the result to the knowledge graph to form a new entity is pushed.The function ofθ coefficients is plotted in Figure 2.Figure 2
Adjustment factor function.The adjustment factor is close to 1 ford values less than 500 km, which has almost no effect on the matching score, and then decays exponentially, and the true score converges to 0 for matching to transport destinations larger than 2000 km.In order to reduce the redundancy, only the top 10% of the results are retained in each query. In order to further reduce the impact of matching to districts and counties with the same name or similar names and to reduce the impact of incorrect word separation results, this paper clusters the matching results by city and obtains the following output in the form of a dictionary.In summary, the techniques used in this paper are summarized in Figure3. As shown in Figure 3, the Chinese address syllabification technique is not only applied in the construction and extension of the knowledge graph update but is also a necessary technique for the full-text search and matching of Chinese addresses. Chinese address full-text search technology is applied to the knowledge graph to both accurately identify the input addresses and automatically expand the knowledge graph. The specific flow of complete address search and matching is shown in Figure 4.Figure 3
Technical route.Figure 4
Address matching process.


## 3.1. Semisupervised Splitting Method

Whether building Chinese full-text indexes or preprocessing source addresses, credible Chinese word separation methods are needed. In this paper, we combine the advantages of lexical word separation and statistical-based word separation methods to avoid a large amount of training and huge dictionary construction requirements by using statistical word separation models on the one hand and to expand the dictionary on a rolling basis based on correctly identified data and threshold judgment mechanism on the other hand. The basic dictionary consists of short name data of national townships and above, administrative regions and enterprise suffix words, and data tables of petrochemical enterprise information in Ningbo [24], and the structure of the dictionary is as follows:(1)vi=w,f,t,where i∈N, vi denote the i-th word, and w, f, and t denote the address short name, word frequency, and the word nature, respectively. When building the basic dictionary, the word frequency is set to 10 by default, and the subsequent successful word separation results and matches will be increased automatically by the program. Only three types of lexical properties are considered in the basic dictionary, namely, ns (place name), hm (enterprise, mainly chemical plant), su (suffix words, such as province, city, district, autonomous region, limited company, and other place name) [25].The prefix dictionary, TRIE dictionary tree, which is a type of hash lookup tree, is constructed before the word division to enable fast dictionary lookup. The DAG is stored in the form of a dictionary with the following structure:(2)Pi:na+nb,⋯,nx,where i,a,b,⋯,x∈N. Pi denotes the index of the i-th word in the input address and na denotes the end position of the a-th divisor of Pi as the prefix that has a word frequency greater than 0. Thus, the DAG records all possible cuts of the input address, and the next step is to find the path with maximum probability based on dynamic programming. The probability of each word is equal to the frequency of the word in the prefix divided by the sum of all word frequencies; if the word frequency is 0 or does not exist, it is set to 1. The probability of each word is calculated as follows:(3)pj=pj,a×pj,b×⋯×pj,x,where j,a,b,⋯,x∈N,pj denotes the probability of the jth cut or the jth path, and pj,a denotes the ath word frequency in the jth path. To facilitate the calculation, the above equation is taken logarithmically to obtain(4)logpj=logpj,a+logpj,b+⋯+logpj,x.In the Marxist address text, the semantic focus comes first, and the dynamic programming method is used to calculate the probabilities of all paths from front to back and select the path with the highest probability among them, that is, to obtain the better partitioning result applicable to the current dictionary [26, 27]. Since the basic lexicon already has more complete data, the above process can already get the correct word separation results for most of the input. In order to make up for the shortcomings of the basic dictionary, we set a minimum probability threshold of Pmin in this paper, so that the cut score result for any input address has(5)maxlogpj<Pmin.Then, the input address is recorded, the word is manually split, and the valid result is added to the dictionary and inserted at the end of the address to be split. If an invalid address is detected, the address is marked as invalid. Based on the calculation of the maximumlogpj for 1000 groups of words, Pmin is set to 80 based on a semisupervised intervention rate of 2%.


## 3.2. Full-Text Search Matching Score Mechanism

The full-text search matching score mechanism is built based on TF-IDF (term frequency-inverse document frequency) [28] technology to calculate the matching relevance of full-text search results, and the scoring function is calculated as follows:(6)sq,d=Nq.coordq,d.tftind.idft.normt,d,where q and d are the text of the query and the matching documents, and N(q) is a predefined normalized query statement, which usually has no effect on a particular query application. coordq,d indicates the number of query responses in a document, and the more query items appear in a document, the better the query matches the document, which is mainly used in a multicriteria query environment. In the address query, the successful query results can be used to build a perfect syllogism dictionary to convert a single query into multiple queries to improve the matching accuracy of q [29].idf denotes inverse document frequency (IDF), which is calculated as follows:(7)idfti=logDj:ti∈dj,where D is the number of documents and j:ti∈dj denotes the total number of words ti in the document dj. The fewer the occurrences of a word in a single document or more documents it appears in, the higher the relevance of the match, which can exclude the influence of various dummy words to some extent. In addition, this element indicates that a certain address gets a higher matching rate when it appears in more entities, so the later section will keep using the successfully matched addresses to extend the knowledge graph in the process of constructing the knowledge graph, so that the probability of partially matching the wrong address by chance decreases as the matching process proceeds.normt,d denotes the field length normalized value, which is related to the result of word separation and can generally be reduced to the reciprocal of the square root of the number of words, implying that the full-text search gives priority to matching long fields, so the long address fields will be established later in the process of building the knowledge graph to improve the success rate of address search and matching.


## 3.3. Knowledge Graph

Knowledge graphs are structured semantic knowledge bases that can well describe concepts and interrelationships in the physical world. The following are some of the new types of entities that are created in the updated knowledge graph: factory, chemical company, and others [30].According to the research of the full-text search for matching score mechanism, in order to improve the accuracy of address matching, the knowledge graph is constructed according to the fields as shown in Table1.Table 1
Knowledge graph field information table.
Corresponding phonetic fieldExplainNamePinyinAddress nameShort nameShort pinyinDesuffixIndex nameShort indexCustom index nameCode—Administrative region codeLocationLocation pinyinNormalized addressShort locationShort location pinyinDesuffix specification addressIn order to correctly parse the address misspellings in hazardous chemical manifest addresses, this paper establishes a field corresponding to the phonetic field with tone to obtain credible search results. In order to obtain more accurate address matching for full-text search, the knowledge graph is constructed by taking into account the influence of the full-text search for matching score calculation function on the full-text indexing results, and the detailed address field location is designed in this paper. The location field and short_location field enable the full-text address search to better avoid matching the wrong renamed addresses [31].The relationships between the knowledge graph entities include the dependent belong, which is expressed as(8)a−rel:belong−>b,where a and b denote entities, rel is a sign word indicating the relationship, belong indicates that the relationship is subordinate, and the arrow indicates the direction of the relationship. In the critical transport address analysis, the navigation distance relationship between cities is also involved:(9)a−rel:distance−b,where a,b∈city is the combination of all entities of type city. The distance relationship does not need to define the direction. Its value is the navigation distance between the city center coordinates calculated using the Gaode Map navigation API.After the basic knowledge graph is established, this paper constructs full-text indexes for Chinese and Pinyin based on the full-text search matching score mechanism described in Section2.2 for all entities in the knowledge graph, where the Chinese index uses the semisupervised word separation method described in Section 2.1, and the Pinyin index uses a simple space (unicode-whitespace) word separation method.


## 3.4. Full-Text Search Is Applied to Address Matching

There are generally two Chinese data items in the single address data of the electronic waybill for hazardous chemicals, one is the actual address and the other is the enterprise address. The actual address is more reliable information, while the enterprise address has limited reliability, and only when the actual address cannot be matched, the enterprise address is used for address search.The value of match score for full-text search results is calculated as follows:(10)score=waSa+1−waSc.Among them,Sa and Sc are the full-text search matching scores of the actual address and enterprise address, respectively; wa is the weight, and this paper mainly takes the matching result of the actual address as the basis, so the value is set to 0.8. Sa, Sc the calculation method is as follows:(11)S=Skanzi×whanzi+Spinyin×1−whanz.Skanzi and Spinyin are the matching score obtained by Chinese and Pinyin full-text search, respectively, and whanz is the weight of Chinese full-text search score, set to 0.8. If the full-text search returns empty results, the score is set to 0.Limited by multiple safety factors such as high-speed control and parking restrictions for dangerous goods, long-distance transportation of dangerous goods accounts for a relatively small percentage. The distance adjustment coefficient is added in the calculation of the matching degree value, which is calculated as follows:(12)Safter=Sbefore.θ.Among them,Safter and Sbefore are the full-text search matching scores before and after adjustment, respectively, and θ is the distance adjustment factor. The calculation is as follows:(13)Ci:li,1:si1,li,2:si,2,⋯,li,n:si,n,where Ci denotes the i-th city, li,n denotes the n-th full-text search result address belonging to Ci, and si,n denotes the full-text search matching score corresponding to li,n. Let(14)s¯=aversi,1,si,2,⋯,si,n,smax=maxsi,1,si,2,⋯,si,n.Letm denotes the number of si,1,si,2,⋯,si,ns greater than s¯, and then, the matching score of city Ci is calculated as follows:(15)SCi=smax·2mn.As can be seen from the above equation, the greater the number of matched results and the larger the score, the greater theSCi is and the higher the error tolerance for partial accidental mismatches. The final matching result is SCi.Ifsi,n≤3, then the match is considered invalid, the address is skipped, and the address is added to the geographic resolution interface of Baidu or Gaode Maps, and the search result is confirmed only if the results output by the two major service providers is consistent; otherwise, it is added to the address to be supervised for classification. If si,n>3, then the matching result is entered, the word frequency of the subword dictionary is updated, and the result to the knowledge graph to form a new entity is pushed.The function ofθ coefficients is plotted in Figure 2.Figure 2
Adjustment factor function.The adjustment factor is close to 1 ford values less than 500 km, which has almost no effect on the matching score, and then decays exponentially, and the true score converges to 0 for matching to transport destinations larger than 2000 km.In order to reduce the redundancy, only the top 10% of the results are retained in each query. In order to further reduce the impact of matching to districts and counties with the same name or similar names and to reduce the impact of incorrect word separation results, this paper clusters the matching results by city and obtains the following output in the form of a dictionary.In summary, the techniques used in this paper are summarized in Figure3. As shown in Figure 3, the Chinese address syllabification technique is not only applied in the construction and extension of the knowledge graph update but is also a necessary technique for the full-text search and matching of Chinese addresses. Chinese address full-text search technology is applied to the knowledge graph to both accurately identify the input addresses and automatically expand the knowledge graph. The specific flow of complete address search and matching is shown in Figure 4.Figure 3
Technical route.Figure 4
Address matching process.


## 4. Results

### 4.1. Multidimensional Scale Analysis

The core idea of multidimensional scale analysis is dimensionality reduction, and the connection between research objects is expressed as a flat distance, which is transferred to coword analysis; that is, the degree of closeness between keywords or subject words is reflected as flat distance. In multidimensional scale analysis, the research objects are represented as points, and the objects with close connection will be clustered into a class group, and the core states objects will be in the middle of the class group. In this paper, we use spss21.0 statistical analysis software to reduce the dimensionality of the information ecology coword dissimilarity matrix and use the multidimensional scale (ALSCAL) tool in the scale function to generate the visual knowledge map as shown in Figure5.Figure 5
Multidimensional scale analysis mapping.The two-dimensional analysis map of multidimensional scale analysis is basically consistent with the knowledge map of cluster analysis, but there are new content structure features. As shown in Figure5, the information ecology high-frequency keywords are clustered and distributed into four more concentrated clusters. According to the internal keyword meanings and characteristics of each cluster, the clusters are divided into the first quadrant in clockwise order, information ecology chain formation mechanism and e-commerce, information library and business website, network information ecosystem evaluation and balance, and information ecology and education informatization. Multidimensional scale analysis mapping is a reaggregation of the cluster analysis mapping. In general, the first quadrant of the multidimensional scale analysis mapping has closely linked themes and is in the center of the research network; the second quadrant has a looser theme structure and has further potential research value; the third quadrant has a tight and structured theme, and there exists formal research by related institutions, but it is at the edge of the research network; the fourth quadrant is less important and is at the edge of the research network [6].


### 4.2. Social Network Analysis

The social network approach is a social research method that expresses the interaction between social actors in the form of a network map. Social networks are collections of nodes and links. Nodes symbolize actors of social networks, people, places, and institutions that can be regarded as social actors, and links represent connections among actors of social networks. In the common word analysis, keywords and subject words play the role of social actors, and they appear in the same article as links between each other and are expressed in the form of links. Generally speaking, the higher the frequency of keywords and keywords in the same document, the more closely they are linked to each other, and the more dense the social network visualization map will be [32].According to the analysis of social network centrality, the knowledge map shown in Figure6 is formed, and the connection between nodes is the co-occurrence relationship of the original matrix, and the size of nodes is proportional to the frequency of co-occurrence network keywords and network status, and the thickness of the connection indicates the strength of keywords.Figure 6
Social network analysis knowledge map.Social network centrality analysis clarifies the general overview and the internal structure of information ecological themes with the help of quantitative indicators such as network density and central potential. Network density reflects the closeness of network members’ connection, and the greater the density, the closer the relationship between network members [7]. In terms of its value, the more the network density tends to be close to 1, the more closely connected the network is. The analyzed data show that the co-occurrence network density is 0.4319 with a standard deviation of 1.32, which is a good density and significant frequency difference. However, if only relying on the network density does not give an interpretation of the degree of density of social network ties, this paper uses the central potential of the social network graph as a supplementary variable of network density. The central potential is the overall principle aggregation degree evaluation, and the central potential of the common word network degree value is 37.89%, which shows that there is a concentration trend of the network. In addition, the graph clearly shows the centrality of information ecology and information ecosystem.The degree centrality of a point is the number of points in the social network that is directly connected to other members. In other words, if a node has established direct connections with many nodes, then that point is considered to have a high degree of point centrality. The analysis results show that the top eight of degree centrality are information ecosystem, information ecology, information ecological chain, information ecological niche, operation mechanism, library, theoretical research, and information ecological environment, which are also well reflected by the size of node area. Through the analysis of the social network information ecology map, it can be concluded that the existing research field of information ecology presents a relatively decentralized pattern. For example, in addition to these hot topics such as information niche and library, other relevant research concerns are relatively scattered.


### 4.3. Integrated Perspective Information Ecology Analysis

Based on the above clustering analysis, multidimensional scale analysis, and social network analysis knowledge mapping expression, the information ecology research themes and content structure can be presented, but each of the three types of mapping has shortcomings only for a certain method, so it is more rigorous and scientific to combine the information of the three types of mapping to present. Cluster analysis mapping aggregates nine categories of information ecology, but the drawback is that it follows the criterion of unique attribution and fails to express the connection with other categories of keywords; multidimensional scale analysis aggregates four categories of research themes and presents location information, but the strength of connection among keywords is not expressed; social network analysis mapping presents the content connection and strength among keywords but does not take into account the clustering analysis categorization. Therefore, based on the above three analyses and asking relevant experts and scholars in the field of information ecology, I manually reorganized the keywords in the field of information ecology research in China and formed the social network mapping of high-frequency keyword reclustering in a comprehensive perspective as shown in Figure7.Figure 7
Knowledge mapping of information ecology research from an integrated perspective.The mapping results in Figure7 show that the core keyword of the information ecology research theme is “information ecology”, which is not only the logical starting point of this paper but also describes the disciplinary affiliation of information ecology and radiates all aspects of information ecology research. The core keyword is surrounded by four research areas composed of closely related keyword clusters, each of which has a relatively key core vocabulary representing such cluster themes. By using the results of cluster analysis, multidimensional scale analysis, and social network analysis, the research fields are integrated with a comprehensive vision, showing “information ecology” as the core, “information ecosystem” as the auxiliary, “information ecological chain “, “information ecological niche”, “information ecological environment” as the main direction of the three-level research trend.


### 4.4. Knowledge Graph Construction Effect

Based on the keyword mapping of Marxist theory journals in 2013 (Figure8), the focus of attention of Marxist theory journals was on the propagation and study of the spirit of the 18th Party Congress. This year was the 120th anniversary of Mao Zedong’s birth, and the study of Mao’s thought became a hot topic. It was also a year when the propaganda and research on the Chinese dream were widely carried out. As an integral part of the propagation and study of the spirit of the 18th CPC National Congress, these two major topics became the focus of attention of Marxist theory journals in this year.Figure 8
Keyword mapping of Marxist theory journals in 2013.Based on the keyword mapping of Marxist theory journals in 2014 (Figure9), hotspots of interest in 2014 were the comprehensive deepening of reform.Figure 9
Keyword mapping of Marxist theory journals in 2014.Interviews are also one of the main methods used by journals to conduct research on the comprehensive deepening of reform. For example, Issue 1 of Scientific Socialism features Professor Yan Jirong, Director of the Department of Political Science, School of Government Management, Peking University, and Researcher He Zengke, Director of the Department of World Development Strategy, Central Compilation and Administration Bureau, on “What is national governance and its modernization? Why should we promote the modernization of national governance? How to promote the modernization of the national governance system and governance capacity? The researcher discussed in depth such questions [33].Based on the keyword mapping of Marxist theory journals in 2015 (Figure10), the hot topic of attention in 2015 was the comprehensive and strict governance of the Party. Since the 18th Party Congress, General Secretary Xi Jinping has put forward many new ideas and requirements from the aspects of “what is a comprehensive and strict governance of the Party”, “why a comprehensive and strict governance of the Party”, “how to achieve a comprehensive and strict governance of the Party”. In December 2014, General Secretary Xi Jinping emphasized the strict comprehensive governance of the party during his research in Jiangsu. In December 2014, General Secretary Xi Jinping emphasized the comprehensive and strict governance of the party during his research in Jiangsu.Figure 10
Keyword mapping of Marxist theory journals in 2015.


## 4.1. Multidimensional Scale Analysis

The core idea of multidimensional scale analysis is dimensionality reduction, and the connection between research objects is expressed as a flat distance, which is transferred to coword analysis; that is, the degree of closeness between keywords or subject words is reflected as flat distance. In multidimensional scale analysis, the research objects are represented as points, and the objects with close connection will be clustered into a class group, and the core states objects will be in the middle of the class group. In this paper, we use spss21.0 statistical analysis software to reduce the dimensionality of the information ecology coword dissimilarity matrix and use the multidimensional scale (ALSCAL) tool in the scale function to generate the visual knowledge map as shown in Figure5.Figure 5
Multidimensional scale analysis mapping.The two-dimensional analysis map of multidimensional scale analysis is basically consistent with the knowledge map of cluster analysis, but there are new content structure features. As shown in Figure5, the information ecology high-frequency keywords are clustered and distributed into four more concentrated clusters. According to the internal keyword meanings and characteristics of each cluster, the clusters are divided into the first quadrant in clockwise order, information ecology chain formation mechanism and e-commerce, information library and business website, network information ecosystem evaluation and balance, and information ecology and education informatization. Multidimensional scale analysis mapping is a reaggregation of the cluster analysis mapping. In general, the first quadrant of the multidimensional scale analysis mapping has closely linked themes and is in the center of the research network; the second quadrant has a looser theme structure and has further potential research value; the third quadrant has a tight and structured theme, and there exists formal research by related institutions, but it is at the edge of the research network; the fourth quadrant is less important and is at the edge of the research network [6].


## 4.2. Social Network Analysis

The social network approach is a social research method that expresses the interaction between social actors in the form of a network map. Social networks are collections of nodes and links. Nodes symbolize actors of social networks, people, places, and institutions that can be regarded as social actors, and links represent connections among actors of social networks. In the common word analysis, keywords and subject words play the role of social actors, and they appear in the same article as links between each other and are expressed in the form of links. Generally speaking, the higher the frequency of keywords and keywords in the same document, the more closely they are linked to each other, and the more dense the social network visualization map will be [32].According to the analysis of social network centrality, the knowledge map shown in Figure6 is formed, and the connection between nodes is the co-occurrence relationship of the original matrix, and the size of nodes is proportional to the frequency of co-occurrence network keywords and network status, and the thickness of the connection indicates the strength of keywords.Figure 6
Social network analysis knowledge map.Social network centrality analysis clarifies the general overview and the internal structure of information ecological themes with the help of quantitative indicators such as network density and central potential. Network density reflects the closeness of network members’ connection, and the greater the density, the closer the relationship between network members [7]. In terms of its value, the more the network density tends to be close to 1, the more closely connected the network is. The analyzed data show that the co-occurrence network density is 0.4319 with a standard deviation of 1.32, which is a good density and significant frequency difference. However, if only relying on the network density does not give an interpretation of the degree of density of social network ties, this paper uses the central potential of the social network graph as a supplementary variable of network density. The central potential is the overall principle aggregation degree evaluation, and the central potential of the common word network degree value is 37.89%, which shows that there is a concentration trend of the network. In addition, the graph clearly shows the centrality of information ecology and information ecosystem.The degree centrality of a point is the number of points in the social network that is directly connected to other members. In other words, if a node has established direct connections with many nodes, then that point is considered to have a high degree of point centrality. The analysis results show that the top eight of degree centrality are information ecosystem, information ecology, information ecological chain, information ecological niche, operation mechanism, library, theoretical research, and information ecological environment, which are also well reflected by the size of node area. Through the analysis of the social network information ecology map, it can be concluded that the existing research field of information ecology presents a relatively decentralized pattern. For example, in addition to these hot topics such as information niche and library, other relevant research concerns are relatively scattered.


## 4.3. Integrated Perspective Information Ecology Analysis

Based on the above clustering analysis, multidimensional scale analysis, and social network analysis knowledge mapping expression, the information ecology research themes and content structure can be presented, but each of the three types of mapping has shortcomings only for a certain method, so it is more rigorous and scientific to combine the information of the three types of mapping to present. Cluster analysis mapping aggregates nine categories of information ecology, but the drawback is that it follows the criterion of unique attribution and fails to express the connection with other categories of keywords; multidimensional scale analysis aggregates four categories of research themes and presents location information, but the strength of connection among keywords is not expressed; social network analysis mapping presents the content connection and strength among keywords but does not take into account the clustering analysis categorization. Therefore, based on the above three analyses and asking relevant experts and scholars in the field of information ecology, I manually reorganized the keywords in the field of information ecology research in China and formed the social network mapping of high-frequency keyword reclustering in a comprehensive perspective as shown in Figure7.Figure 7
Knowledge mapping of information ecology research from an integrated perspective.The mapping results in Figure7 show that the core keyword of the information ecology research theme is “information ecology”, which is not only the logical starting point of this paper but also describes the disciplinary affiliation of information ecology and radiates all aspects of information ecology research. The core keyword is surrounded by four research areas composed of closely related keyword clusters, each of which has a relatively key core vocabulary representing such cluster themes. By using the results of cluster analysis, multidimensional scale analysis, and social network analysis, the research fields are integrated with a comprehensive vision, showing “information ecology” as the core, “information ecosystem” as the auxiliary, “information ecological chain “, “information ecological niche”, “information ecological environment” as the main direction of the three-level research trend.


## 4.4. Knowledge Graph Construction Effect

Based on the keyword mapping of Marxist theory journals in 2013 (Figure8), the focus of attention of Marxist theory journals was on the propagation and study of the spirit of the 18th Party Congress. This year was the 120th anniversary of Mao Zedong’s birth, and the study of Mao’s thought became a hot topic. It was also a year when the propaganda and research on the Chinese dream were widely carried out. As an integral part of the propagation and study of the spirit of the 18th CPC National Congress, these two major topics became the focus of attention of Marxist theory journals in this year.Figure 8
Keyword mapping of Marxist theory journals in 2013.Based on the keyword mapping of Marxist theory journals in 2014 (Figure9), hotspots of interest in 2014 were the comprehensive deepening of reform.Figure 9
Keyword mapping of Marxist theory journals in 2014.Interviews are also one of the main methods used by journals to conduct research on the comprehensive deepening of reform. For example, Issue 1 of Scientific Socialism features Professor Yan Jirong, Director of the Department of Political Science, School of Government Management, Peking University, and Researcher He Zengke, Director of the Department of World Development Strategy, Central Compilation and Administration Bureau, on “What is national governance and its modernization? Why should we promote the modernization of national governance? How to promote the modernization of the national governance system and governance capacity? The researcher discussed in depth such questions [33].Based on the keyword mapping of Marxist theory journals in 2015 (Figure10), the hot topic of attention in 2015 was the comprehensive and strict governance of the Party. Since the 18th Party Congress, General Secretary Xi Jinping has put forward many new ideas and requirements from the aspects of “what is a comprehensive and strict governance of the Party”, “why a comprehensive and strict governance of the Party”, “how to achieve a comprehensive and strict governance of the Party”. In December 2014, General Secretary Xi Jinping emphasized the strict comprehensive governance of the party during his research in Jiangsu. In December 2014, General Secretary Xi Jinping emphasized the comprehensive and strict governance of the party during his research in Jiangsu.Figure 10
Keyword mapping of Marxist theory journals in 2015.


## 5. Conclusions

Using bibliographic coword analysis methods and related visualization techniques, this paper depicts the knowledge map of domestic Marx research in recent years and outlines the overall situation of domestic information ecology. The relevant themes are explored based on qualitative and quantitative analyses, which are of great significance for the development of the discipline. The key hot spot in the Internet era is information transfer, and the current information ecology research has shown networked features, with high-frequency keywords expressing information ecology networked combination, such as networked information ecosystem and networked information ecological chain. However, combining of mapping and literature shows that information ecology research in the network era has not yet entered the standardized research stage, and more theoretical and applied research only stays in the networked era background and information ecology superficial concept combination, and its related mechanism and operational characteristics still need the deep deduction.



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*Source: 3806576-2022-03-30.xml*