Domestic cigar content marketing high-value hotspot theme identification method based on social network data demand ontology

Abstract

The invention provides a domestic cigar content marketing high-value hot topic identification method based on a social network data demand ontology, and the method comprises the steps: carrying out the collection of Baidu index consumer social network search and query data, building a social network search and query dynamic data set through the data arrangement and updating, and carrying out the recognition of the high-value hot topic of the domestic cigar content marketing. Forming a cigar consumer demand initial domain ontology; on the basis, network text analysis is adopted, a cigar consumer demand evolution ontology corpus is constructed, and a cigar consumer evolution demand field ontology is formed; and finally, a label index statistical method and a high-value hotspot label identification method are adopted to identify consumer hotspot themes, decision support is provided for brand content marketing theme selection, and an important reference basis is provided for brand construction and market subdivision in digital marketing in the cigar industry.

Description

Technical Field

[0001] The present invention relates to a computer technology, and in particular to a method and device for identifying high-value hot topics in domestic cigar content marketing based on social network data demand ontology. Background Art

[0002] With the continuous expansion of high-end consumer groups and the continuous upgrading of consumption concepts, Chinese cigars have shown a sustained growth trend, but objectively speaking, the market potential and consumption momentum of cigar consumption have not been tapped.

[0003] Content marketing is an important means of brand communication for domestic cigars. Some of the potential consumers of Chinese cigars come from cigarette consumers. Through effective marketing strategies, brand building and relationship management, we can promote the category and brand conversion of potential cigar consumers, achieve sales growth of Chinese cigars and enhance brand loyalty. Social network search query data (social network data) has become an important data source for cigar consumer behavior research.

[0004] Therefore, how to mine consumer demand themes in social network data, obtain consumer demand data, screen out target consumers, and provide decision-making for cigar brand content marketing becomes a technical issue. Summary of the invention

[0005] In order to solve the above technical problems, the present invention provides a method and device for identifying high-value hot topics in domestic cigar content marketing based on social network data demand ontology.

[0006] The method for identifying high-value hot topics in domestic cigar content marketing based on social network data demand ontology of the present invention uses a computer device composed of a processor and a memory, and the processor calls a calculation program in the memory to complete the calculation steps. The steps are as follows: S1: Collect Baidu Index consumer social network search query data.

[0007] S2: By organizing the search query data, a social network search query dynamic data set is constructed to form an initial demand ontology of cigar consumers; the demand ontology refers to a demand term set.

[0008] S3: Use network text analysis to identify the implicit needs of potential cigar consumers and transform the initial demand ontology of cigar consumers into the evolved demand ontology of cigar consumers.

[0009] S4: Through the evolutionary demand ontology of cigar consumers, a label system that adapts to market changes is constructed, and consumer hot topics are identified using label indicator statistics and high-value hot label identification methods.

[0010] As described above, the method for identifying high-value hot topics in domestic cigar content marketing based on social network data demand ontology collects Baidu Index consumer social network search query data, and the specific steps are as follows: Candidate keywords related to cigars are established, and network search terms are determined through the candidate keywords. Demand graph data of the previous week is crawled on the Internet through the network search terms, and stored in a local document according to the corresponding data dictionary specifications; wherein the data file is a tabular data formed by reconstructing the data, and the document is saved in the name format of the search term and date.

[0011] The method for identifying high-value hot topics in domestic cigar content marketing based on social network data demand ontology as described above, wherein the search query data is collated to construct a social network search query dynamic data set to form the initial demand ontology of cigar consumers, specifically: (1) The search query data is merged, and the searches of the same type of keywords at different times and with different inputs are merged to construct the initial demand ontology of cigar consumers. The initial demand ontology of cigar consumers is normalized to form a dynamic data set of social network search queries.

[0012] (2) Automatically update the search keywords related to the demand graph every week. During the update process, if the initial table contains updated data, it will be directly processed according to the standardized reference table and merged into the search query dynamic data set; if not, the associated keywords will be re-determined and standardized, and the standardized reference terms will be updated to the search query dynamic data set.

[0013] As described above, network text analysis is used to identify the implicit needs of potential cigar consumers and transform the initial demand ontology of cigar consumers into the evolved demand ontology of cigar consumers, specifically: S41: Preprocessing the data of the initial demand ontology of cigar consumers, cleaning, converting and organizing the original data; standardizing the initial demand ontology of cigar consumers; the standardization processing includes three steps: screening out available data and standardizing related keywords, and normalizing.

[0014] S42: Classify the initial demand ontology of cigar consumers and construct a hierarchical structure of the evolutionary demand ontology of cigar consumers. Specifically, the evolutionary demand ontology of cigar consumers is divided into 8 categories, including brand, knowledge, channel, consumption attribute, matching category, life preference, culture, and innovation; and then divided into 53 subcategories at the lower level; complete the construction of the corpus and form the evolutionary demand ontology of cigar consumers.

[0015] The method for identifying high-value hot topics in domestic cigar content marketing based on social network data demand ontology as described above includes: The brand is defined as cigar brand information, tobacco company, and category information; The knowledge is defined as relevant information that consumers want to know through search; The channels are defined as the channels through which consumers learn relevant information and purchase products; The consumption attributes are defined as the scenarios of cigar consumption recognized by consumers; The said pairing category is defined as the correlation of cigars with other categories; The life preferences are defined as the distribution of interests in crowd attributes; Said culture is defined as the cigar cultural concept recognized by consumers; The innovation is defined as the taste of Chinese cigars based on consumers' brand selection needs.

[0016] The method for identifying high-value hot topics in domestic cigar content marketing based on social network data demand ontology as described above, wherein, as described, through the cigar consumer evolution demand ontology, a label system that adapts to market changes is constructed, and the label indicator statistics method and the high-value hot label identification method are used to identify consumer hot topics, specifically: The evolutionary demand ontology of cigar consumers is described in a standardized manner, and the data is divided into cigar demand tags.

[0017] In the cigar demand tags, high-value hot topics are identified by adopting the tag indicator statistics method and the high-value hot tag identification method.

[0018] Among them, the label index statistical method refers to identifying high-value labels in cigar demand labels by counting the sample number, PV index and SIM index of the label.

[0019] The method for identifying high-value hot topics in domestic cigar content marketing based on social network data demand ontology as described above, wherein the label index statistical method specifically adopts the RFM model to score the cigar demand labels, and identifies hot topics based on the scoring results, specifically: The RFM model is used to identify high-value tags within the ontology of the evolutionary needs of cigar consumers. The RFM model specifically includes: citing the weights of the three indicators of proximity, frequency, and value to achieve dynamic weights. The analytic hierarchy process is used to determine the optimal weight parameters to achieve keyword value segmentation within the ontology of the evolutionary needs of cigar consumers, obtain the key to high-value tags, and use high-value keywords as high-value hot topics for content marketing.

[0020] As described above, the method for identifying high-value hot topics in domestic cigar content marketing based on social network data demand ontology, wherein the RFM model is to set up a cigar demand tag, calculate the RFM value of the associated keywords in the tag, and obtain the RFM value of the cigar demand tag. The RFM model is as follows: Among them, R represents the time distance from the most recent appearance of the associated keyword to the data cutoff; F represents the total frequency of the associated keyword appearing within the time span; M represents the SIM value of the associated keyword and the network search term, is the weight of R, is the weight of F, is the weight of M, the weight range is (0,1), and satisfies .

[0021] As described above, the method for identifying high-value hot topics in domestic cigar content marketing based on social network data demand ontology, in which the RFM model evaluation results are divided into five result levels, specifically: Assign high-value tags with 10≤RFM; Assign a general value label with 4≤RFM<10; Assign the important development label when 2≤RFM<4; Assign a general development label with 1≤RFM<2; Assign low value labels when RFM < 1.

[0022] The device for identifying high-value hot topics in the content marketing of domestic cigars based on the social network data demand ontology of the present invention is a computer device composed of a processor and a memory, wherein the processor calls a calculation program in the memory to complete the calculation steps, and the calculation program is used to execute the method for identifying high-value hot topics in the content marketing of domestic cigars based on the social network data demand ontology as described above. The computer device has: The crawler module is used to collect Baidu Index consumer social network search query data.

[0023] The processing module is used to organize the search query data, construct a social network search query dynamic data set, and form an initial demand ontology of cigar consumers; the demand ontology refers to a demand term set.

[0024] The conversion module is used to identify the implicit needs of potential cigar consumers and transform the initial demand ontology of cigar consumers into the evolved demand ontology of cigar consumers.

[0025] The output module is used to build a label system that adapts to market changes through the evolutionary demand ontology of cigar consumers, and to identify consumer hot topics using label indicator statistics and high-value hot label identification methods.

[0026] The effective technical effects of the present invention are as follows: by collecting Baidu Index consumer social network search query data, constructing a social network search query dynamic data set through data sorting and updating, and forming an initial domain ontology of cigar consumer demand; on this basis, using network text analysis to identify the implicit needs of potential cigar consumers, constructing a cigar consumer demand evolution ontology corpus, and forming a cigar consumer evolution demand domain ontology; finally, based on the cigar demand evolution domain ontology engineering, a label system that adapts to market changes is constructed, and a label indicator statistical method and a high-value hot label identification method are used to identify consumer hot topics, providing decision support for brand content marketing theme selection.

[0027] The method of the present invention obtains accurate data, has a simple algorithm, and occupies less computer system resources. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] Figure 1 It is the Baidu Index web crawler process; Figure 2 It is the ontological structure diagram of the evolutionary needs of cigar consumers. DETAILED DESCRIPTION

[0029] In the present invention: "Demand ontology" is a special type of terminology set that expresses demand. Among them, "Cigar Consumer Initial Demand Ontology" is a special type of terminology set that expresses the initial consumer demand; "Cigar Consumer Evolved Demand Ontology" is a special type of terminology set of consumer demand obtained by optimizing "Cigar Consumer Initial Demand Ontology".

[0030] "Tag" refers to the business encapsulation of physical layer data information items and is a logical definition of data.

[0031] "Content marketing" refers to a strategy that achieves online marketing goals by creating, publishing and disseminating valuable content to attract and influence target customers. "Keywords" are words that reflect the decisive role of "actors" in social networks. They are words that must be included in the content when searching for information on the Internet. They specifically refer to the words used by a single media when making and using indexes, such as "cigars", "long X cigars", etc. "Internet search terms" refer to words or phrases that users enter to describe the information or content they need when searching for information on search websites.

[0032] Example 1: The implementation of the method and / or system of an embodiment of the present invention may involve performing or completing the selected task manually, automatically, or in a combination thereof. For example, the hardware for performing the selected task according to an embodiment of the present invention may be implemented as a chip or circuit. The selected task according to an embodiment of the present invention may be implemented as a plurality of software instructions executed by a computer using any appropriate operating system. In an exemplary embodiment of the present invention, one or more tasks according to the exemplary embodiment of the method and / or system as described herein are performed by a data processor, such as a computing platform for executing a plurality of instructions. Optionally, the data processor includes a volatile storage for storing instructions and / or data and / or a non-volatile storage for storing instructions and / or data, such as a magnetic hard disk and / or a removable medium. A display and / or user input device, such as a keyboard or a mouse, is also provided.

[0033] The computer-readable medium may be a computer-readable signal medium or a computer-readable storage medium, including the following: an electrical connection having one or more conductors, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0034] Computer-readable signal media carries computer-readable program codes. These computer program instructions are stored in computer-readable media, and these instructions make computers, other programmable data processing devices or other devices work in a specific manner, so that the codes stored in the computer-readable media produce instructions of the algorithm of the present invention.

[0035] The device of the present invention is a computer device composed of a processor and a memory, wherein the processor calls a calculation program in the memory to complete the calculation steps, and the computer device has: Crawler module: used to collect Baidu Index consumer social network search query data.

[0036] Processing module: used to organize the search query data, construct a social network search query dynamic data set, and form an initial demand ontology of cigar consumers; the demand ontology refers to a demand term set.

[0037] Conversion module: used to identify the implicit needs of potential cigar consumers and transform the initial demand ontology of cigar consumers into the evolved demand ontology of cigar consumers.

[0038] Output module: It is used to build a label system that adapts to market changes through the evolution of cigar consumers' demand ontology, and identify consumer hot topics using label indicator statistics and high-value hot label recognition methods.

[0039] Example 2: The method for identifying high-value hot topics in domestic cigar content marketing based on social network data demand ontology of the present invention is implemented by executing the following steps 1 to 4 by a computer.

[0040] Step 1: Collect Baidu Index consumer social network search query data; establish candidate keywords related to cigars, and determine network search terms through candidate keywords.

[0041] Among them, the data of the present invention comes from Baidu Index search engine. Baidu search engine data includes Baidu Index data, demand map data and crowd attribute data. The demand map data reflects the related search term demand shown by consumers in the changes of related search behavior of searching for the search term, which can better reflect other needs of users who pay attention to the keyword and belongs to the social network data type studied by the present invention. In the Baidu Index demand map interface, three data of related keywords can be obtained: search change rate (Ratio) and related keyword relevance (sim), and related keyword search popularity (pv).

[0042] Search change rate (RATIO): It is calculated by comprehensively calculating the relevance between keywords and related words, as well as the search demand size of the related words themselves. The distance between the related words and the center of the circle indicates the strength of the relevance of the related words; the size of the related words themselves indicates the size of the search index of the related words themselves.

[0043] Related keyword relevance (SIM): The relevance index between search terms and related terms, refers to the degree of relevance between search terms and related terms, which is directly provided by the distance value of the Baidu Index Demand Map-Demand Related Words interface. The closer the distance, the higher the relevance.

[0044] Search popularity (PV): page views. Related words (PV) refer to the page views of related words, indicating the popularity of related words.

[0045] Combining the CNKI database, industry terminology and Baidu search candidate words, a total of candidate keywords were selected as shown in Table 1 below.

[0046] Table 1 Candidate keywords The demand graph data of the previous week is crawled on the Internet through online search terms, and stored in local documents according to the corresponding data dictionary specifications; Baidu Index data is collected through web crawlers, and the Baidu data obtained are all structured data. The Baidu Index web crawler process is as follows, refer to Figure 1 : (1) After the crawler program is started, it automatically reads and enters the Baidu Index homepage.

[0047] (2) Read the local configuration table and load user data. Load candidate keyword data and corresponding time and space parameters.

[0048] (3) Read the current date and use the date function to calculate the day of the week. The file is saved with the name "search term + date" to facilitate management and later data processing.

[0049] (4) Crawl the demand graph data of the previous week according to the current date, and store it in a local document according to the corresponding data dictionary specification. The data file is the tabular data formed by reconstructing the data, and the document is saved in the name format of the search term and date.

[0050] Step 2: By sorting the search query data: Since the data table obtained by the crawler is in weeks, and the same type of network search terms contains multiple words, based on this, the data is merged, and the different time and different input searches of the same type of keywords are merged to construct the initial demand ontology of cigar consumers, and the initial demand ontology of cigar consumers is normalized to form a social network search query dynamic data set.

[0051] The demand graph-related search keywords are automatically updated every week. During the update process, if the initial table contains updated data, it is directly processed according to the standardized reference table and merged into the search query dynamic data set. If not, the associated keywords are re-determined and standardized, and the standardized reference terms are updated to the search query dynamic data set.

[0052] Step 3: Use network text analysis to identify the implicit needs of potential cigar consumers and transform the initial demand ontology of cigar consumers into the evolved demand ontology of cigar consumers, specifically: Step 3.1: Preprocess the data of the initial demand of cigar consumers, clean, transform and organize the raw data; it includes cleaning, transforming and organizing the raw data to ensure data quality and availability. The raw data often contains missing values, outliers, duplicate values ​​and other problems. Improve data quality through data preprocessing.

[0053] By preprocessing the data for conversion, such as logarithmic transformation, normalization, standardization, etc., it can be ensured that the data meets the assumptions and requirements of the model. The present invention performs outlier removal, text standardization and normalization according to the particularity of the acquired data.

[0054] The standardization of the initial demand ontology of cigar consumers includes three steps: screening out available data, standardizing related keywords, and normalizing. Specifically, it includes: Standardization of cigar consumer initial demand ontology: After the initial ontology of the cigar demand domain is constructed, the original data of cigar social network search queries needs to be standardized: (1) In the initial table of demand map data, cigar and cigarette brand specifications usually appear. Tobacco industry enterprises have multiple brands, and each brand has multiple specifications. Due to the diversity of search terms, in order to facilitate the calculation of various data labels and analyze the changes in the operating market based on specifications in the later stage, the same specification of cigars and cigarettes is standardized by adding prefixes. The standardization method is shown in Table 2.

[0055] Table 2 Standardized reference table (2) Normalization is performed to compress all data within the range of [0, 1], so that the mathematical units between data remain consistent. When a data is exactly the minimum value, it is normalized to 0; if the data is exactly the maximum value, it is normalized to 1. Taking cigar as an example, the processed data is shown in Table 3.

[0056] Table 3 Normalization results of the online search term “cigar” Step 3.2: Classify the initial demand ontology of cigar consumers, construct the hierarchical structure of the cigar consumer evolutionary demand ontology, complete the construction of the corpus, and form the cigar consumer evolutionary demand ontology.

[0057] The initial demand ontology of cigar consumers is transformed into the evolutionary demand ontology of cigar consumers. The evolutionary demand ontology of cigar consumers is divided into 3 layers. The first layer is divided into 8 categories, and there are 53 subcategories under the first layer. Figure 2 .

[0058] The specific definitions of the first level 8 categories of the cigar consumer evolution demand ontology are as follows: Brand: cigar and cigarette related brand information, tobacco companies, category information, etc.; Knowledge: The relevant information that consumers want to know through search; Channel: the channel through which consumers learn relevant information and purchase products; Consumption attributes: In what scenarios are cigars consumed and recognized? Pairing categories: What categories are cigars related to? Lifestyle preferences: Combined with the Baidu Index crowd attribute - interest distribution, it gives the current consumer life preferences, which is an important reference data for cigar content marketing; Culture: cultural concepts.

[0059] Innovation: Consumers are most concerned about health-related issues when it comes to cigars, and ingredients are an important indicator. Taste is a key factor in consumers' brand selection based on individual needs.

[0060] Through the above social network data processing, a corpus was constructed and the ontology of cigar consumer evolutionary needs was formed, as shown in Table 4.

[0061] Table 4 Ontology of evolving needs of cigar consumers (term set or corpus) Step 4: Through the evolutionary demand ontology of cigar consumers, a label system that adapts to market changes is constructed, and consumer hot topics are identified using label indicator statistics and high-value hot label recognition methods.

[0062] The evolutionary demand ontology of cigar consumers is described in a standardized manner, and the data is divided into cigar demand tags.

[0063] The cigar demand label system is based on the cigar consumer demand evolution ontology corpus, combined with the label category system construction method, with the cigar demand evolution domain ontology as an important reference basis, and the standardized description of the concepts, terms and their interrelationships in the tobacco and social search fields, and the data is divided into demand labels. Since the present invention is aimed at brand content marketing needs, the label construction varies according to different application scenarios, and finally 53 labels for each network search term are constructed, as shown in Table 5.

[0064] Table 5 Label construction results table Based on the cigar demand label system, the label index statistics method and high-value hot label identification method are used to identify high-value hot topics. The label index statistics method is based on the construction of the cigar demand label system. By counting the number of label samples, PV index and SIM index, high-value labels can be identified. The high-value hot label identification method uses an improved RFM model, introduces the RFM model into the demand label to build a "high-value model", scores the demand label based on the RFM "high-value model", and identifies hot topics based on the scoring results. The details are as follows: 1. High-value tag identification based on tag indicator statistics.

[0065] The statistical method is based on the construction of the cigar demand label system. By counting the sample number, PV index and SIM index of the label, high-value labels can be identified. Through data calculation, the label results of each network search term are shown in Table 6 below.

[0066] Table 6 Statistics of three-level labels for cigars 2. High-value tag identification based on high-value hotspot tag identification method.

[0067] Through the cigar demand label system, the RFM model is adopted to identify the high-value labels of cigar consumers within a specified time period.

[0068] Based on the construction of keyword RFM model, the present invention fully considers the weights of the three indicators of proximity, frequency and value, realizes dynamic weights, combines the hierarchical analysis method, determines the optimal weight parameters, realizes keyword value segmentation, and identifies high-value keywords.

[0069] (2.1) Construct RFM model.

[0070] The present invention introduces the RFM model into the cigar demand label to construct a "high-value model", takes the demand label as the research object, calculates the RFM value of the associated keywords in the label, and obtains the RFM value of the demand label, thereby identifying its value and predicting the development trend of the corresponding label. In the conventional RFM model, R represents the time distance from the most recent appearance of the associated keyword to the data cutoff; F represents the total frequency of the associated keywords appearing within the time span; M represents the SIM value of the associated keywords and the network search terms. In the conventional RFM model, the present invention introduces a weighted average in order to comprehensively measure the value of each label, and provides the calculation method of the RFM of the present invention. The weights of each indicator are obtained according to the hierarchical analysis method, and then the RFM values ​​of each label are calculated by adding them, that is: In the above formula, R, F, and M are the proximity, frequency, and value of the keyword respectively; are the weights of R, F, and M, ranging from (0,1) and satisfying .

[0071] (2.2) Calculate the RFM value. Through data calculation, the RFM results of each network search term are shown in Table 7 below.

[0072] Table 7 Statistics of RFM indicators for cigar three-level label improvement (2.3) Definition of label value hierarchy.

[0073] After the labels are divided into levels, the label value levels are defined as 8 value types, including important and general value customers, important development and retention customers, general development and retention customers, and important and general retention customers. The label value level is divided into 5 levels, and its definition is shown in Table 8. The calculated RFM index is substituted into the table to obtain a high-value label. It can be seen that "film, entertainment" is a high-value label.

[0074] Table 8 Tag value level definition Marketing content theme selection. Tag system content marketing theme selection is based on high-value tag identification. Through the above research methods, consumers' high-value hot tags are identified to help optimize brand marketing strategies. Through the tag system content marketing theme selection, you can find out which tags can resonate with and interest target consumers, thereby optimizing content marketing strategies and improving the relevance and conversion rate of marketing activities. Theme selection of tags can provide data-driven decision-making basis and formulate more forward-looking market strategies.

[0075] The above are specific examples of the present invention, and the purpose, technical solutions and beneficial effects of the present invention are further described in detail. It should be understood that the above are only specific embodiments of the present invention and are not used to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the protection scope of the present invention. In the above examples, each example is not a single example. If there is a part that is not fully explained in a certain example, other examples can be referred to. Each example can be reasonably combined to form a new example that can achieve the purpose of the present invention.

Claims

1. A method for identifying high-value hot topics in domestic cigar content marketing based on social network data demand ontology, wherein a computer device composed of a processor and a memory is used, and the processor calls a calculation program in the memory to complete the calculation steps, and the method is characterized in that: The steps are as follows: S1: Collect Baidu Index consumer social network search query data; S2: By collating the search query data, a dynamic data set of social network search queries is constructed to form an initial demand ontology of cigar consumers; the demand ontology refers to a demand term set; S3: Use network text analysis to identify the implicit needs of potential cigar consumers and transform the initial demand ontology of cigar consumers into the evolved demand ontology of cigar consumers; S4: Through the evolutionary demand ontology of cigar consumers, a label system that adapts to market changes is constructed, and consumer hot topics are identified using label indicator statistics and high-value hot label identification methods.

2. The method for identifying high-value hot topics in domestic cigar content marketing based on social network data demand ontology as claimed in claim 1, characterized in that: Collect Baidu Index consumer social network search query data. The specific steps are as follows: Candidate keywords related to cigars are established, and network search terms are determined through the candidate keywords. Demand graph data of the previous time period is crawled on the Internet through the network search terms, and stored in a local document after being sorted according to the specifications; wherein the data file is a tabular data formed by reconstructing the data, and the document is saved in the name format of the search term and date.

3. The method for identifying high-value hot topics in domestic cigar content marketing based on social network data demand ontology as claimed in claim 1, characterized in that: The search query data is collated to construct a social network search query dynamic data set to form an initial demand ontology of cigar consumers, specifically: (1) Merge the search query data, merge the searches of the same type of keywords at different times and with different inputs to construct the initial demand ontology of cigar consumers, and normalize the initial demand ontology of cigar consumers to form a dynamic data set of social network search queries; store it in the initial table; (2) Automatically update the search keywords related to the demand graph every week. During the update process, if the initial table contains updated data, it will be directly processed according to the standardized reference table and merged into the search query dynamic data set; If not, the keyword is re-determined and standardized, and the standardized reference terms are updated to the search query dynamic data set.

4. The method for identifying high-value hot topics in domestic cigar content marketing based on social network data demand ontology as claimed in claim 1, characterized in that: The network text analysis is used to identify the implicit needs of potential cigar consumers and transform the initial demand ontology of cigar consumers into the evolved demand ontology of cigar consumers, specifically: S41: pre-processing the data of the initial demand ontology of cigar consumers, cleaning, converting and arranging the original data; standardizing the initial demand ontology of cigar consumers; the standardization processing includes three steps of screening out available data and standardizing related keywords and normalizing processing; S42: Classify the initial demand ontology of cigar consumers and construct the hierarchical structure of the evolutionary demand ontology of cigar consumers, specifically: the evolutionary demand ontology of cigar consumers is divided into 8 categories, including brand, knowledge, channel, consumption attribute, matching category, life preference, culture, and innovation; and then divided into 53 subcategories at the lower level; The construction of the corpus was completed, and the ontology of the evolutionary needs of cigar consumers was formed.

5. The method for identifying high-value hot topics in domestic cigar content marketing based on social network data demand ontology as claimed in claim 4, characterized in that: The brand is defined as cigar brand information, tobacco company, and category information; The said knowledge is defined as the relevant information that consumers want to know through searching; The channels are defined as the channels through which consumers learn relevant information and purchase products; The consumption attributes are defined as the scenarios of cigar consumption recognized by consumers; The said pairing category is defined as the correlation of cigars with other categories; The life preferences are defined as the distribution of interests in crowd attributes; Said culture is defined as the cigar cultural concept recognized by consumers; The innovation is defined as the taste of Chinese cigars based on consumers' brand selection needs.

6. The method for identifying high-value hot topics in domestic cigar content marketing based on social network data demand ontology as claimed in claim 1, characterized in that: The above-mentioned method constructs a label system that adapts to market changes through the evolutionary demand ontology of cigar consumers, and uses the label indicator statistics method and the high-value hot label identification method to identify consumer hot topics, specifically: The ontology of cigar consumer evolutionary demand is standardized and the data is divided into cigar demand labels. In the cigar demand tags, high-value hot topics are identified by using tag indicator statistics and high-value hot tag identification methods; Among them, the label index statistical method refers to identifying high-value labels in cigar demand labels by counting the sample number, PV index and SIM index of the label.

7. The method for identifying high-value hot topics in domestic cigar content marketing based on social network data demand ontology as claimed in claim 6, characterized in that: in, The label index statistical method specifically adopts the RFM model to score the cigar demand labels and identify hot topics based on the scoring results, specifically: The RFM model is used to identify high-value tags within the ontology of the evolutionary needs of cigar consumers. The RFM model specifically includes: citing the weights of the three indicators of proximity, frequency, and value to achieve dynamic weights. The analytic hierarchy process is used to determine the optimal weight parameters to achieve keyword value segmentation within the ontology of the evolutionary needs of cigar consumers, obtain the key to high-value tags, and use high-value keywords as high-value hot topics for content marketing.

8. The method for identifying high-value hot topics in domestic cigar content marketing based on social network data demand ontology as claimed in claim 7, characterized in that: The RFM model is to set up a cigar demand tag, calculate the RFM value of the associated keywords in the tag, and obtain the RFM value of the cigar demand tag. The RFM model is as follows: Among them, R represents the time distance from the most recent appearance of the associated keyword to the data cutoff; F represents the total frequency of the associated keyword appearing within the time span; M represents the SIM value of the associated keyword and the network search term, is the weight of R, is the weight of F, is the weight of M, the weight range is (0,1), and satisfies .

9. The method for identifying high-value hot topics in domestic cigar content marketing based on social network data demand ontology as claimed in claim 9, characterized in that: The RFM model evaluation results are divided into five result levels, specifically: Assign high-value tags with 10≤RFM; Assign a general value label with 4≤RFM<10; Assign the important development label when 2≤RFM<4; Assign a general development label with 1≤RFM<2; Assign low value labels when RFM < 1.

10. A device for identifying high-value hot topics in domestic cigar content marketing based on social network data demand ontology, a computer device composed of a processor and a memory, wherein the processor calls a calculation program in the memory to complete the calculation steps, characterized in that: The computing program is used to execute the method according to any one of claims 1 to 9; The computer device comprises: The crawler module is used to collect Baidu Index consumer social network search query data; A processing module, for collating the search query data, constructing a social network search query dynamic data set, and forming an initial demand ontology of cigar consumers; the demand ontology refers to a demand term set; The transformation module is used to identify the implicit needs of potential cigar consumers and transform the initial demand ontology of cigar consumers into the evolved demand ontology of cigar consumers; The output module is used to build a label system that adapts to market changes through the evolutionary demand ontology of cigar consumers, and to identify consumer hot topics using label indicator statistics and high-value hot label identification methods.

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