Domain name recommendation method and device, equipment and storage medium

Abstract

The invention relates to a domain name recommendation method and device, equipment and a storage medium. The method comprises the following steps: acquiring enterprise name information input by a user; searching an initial domain name recommendation word from a domain name recommendation word database according to the enterprise name information; performing multi-dimensional labeling on the initial domain name recommendation word to obtain a labeled domain name recommendation word; and according to the labeled domain name recommendation word, performing domain name recommendation on the user. By adopting the method, the accuracy of domain name recommendation can be improved.

Description

Technical Field

[0001] This application relates to the field of domain name service technology, and in particular to a domain name recommendation method, apparatus, device, and storage medium. Background Technology

[0002] Users apply to a domain name management organization and obtain the right to use a domain name. The domain name management organization then provides domain name registration services to the users.

[0003] Currently, many domain name management agencies have significant shortcomings in their domain name recommendation functions, with generally low accuracy in recommending keywords. Summary of the Invention

[0004] Based on this, this application provides a domain name recommendation method, apparatus, device, and storage medium to improve the accuracy of domain name recommendations.

[0005] Firstly, a domain name recommendation method is provided, which includes: Obtain the company name information entered by the user; Based on the company name information, search for initial domain name recommendation keywords from the domain name recommendation keyword database; The initial domain name recommendation terms are annotated in multiple dimensions to obtain annotated domain name recommendation terms; and Based on the domain name recommendation keywords marked, domain names are recommended to users.

[0006] Secondly, a domain name recommendation device is provided, the device comprising: The information acquisition module is used to acquire the company name information input by the user; The search module is used to search for initial domain name recommendations from the domain name recommendation keyword database based on the company name information; The annotation module is used to perform multi-dimensional annotation on the initial domain name recommendation terms to obtain annotated domain name recommendation terms; and The recommendation module is used to recommend domain names to users based on the domain name recommendation keywords marked on the domain name.

[0007] In some embodiments, the domain name recommendation device further includes: The basic data acquisition module is used to acquire basic data related to the company's brand. The formal compliance screening module is used to screen the formal compliance of the brand-related basic data, and to obtain domain name recommendations that meet the formal compliance requirements as the first domain name recommendations; and The database creation module is used to create the domain name recommendation term database based on the first domain name recommendation terms.

[0008] In some embodiments, the basic data acquisition module includes: The first acquisition submodule is used to acquire the enterprise's basic data, intellectual property data, brand system data, product system data, and digital channel data; and The second acquisition submodule is used to acquire the brand-related basic data from the main body basic data, the intellectual property basic data, the brand system basic data, the product system basic data, and the digital channel basic data.

[0009] In some embodiments, the annotation module includes: The content compliance screening submodule is used to screen the content of the initial domain name recommendation keywords for compliance, and obtain domain name recommendation keywords that meet the content compliance requirements as the second domain name recommendation keywords; and The multi-dimensional annotation submodule is used to perform multi-dimensional annotation on the recommended words of the second domain name to obtain the annotated recommended words of the domain name.

[0010] In some embodiments, the annotation module includes: The classification index acquisition submodule is used to obtain the classification index of the recommended words for the second domain name based on the registration status of the recommended words for the second domain name; The risk index acquisition submodule is used to obtain the risk index of the recommended words for the second domain name based on the classification weight and the classification index.

[0011] In some embodiments, the annotation module includes: The frequency acquisition submodule is used to obtain the total frequency of the recommended keywords of the second domain name in different enterprises; The extreme value acquisition submodule is used to obtain the common brand extreme value of the second domain name recommendation term based on the total frequency of the second domain name recommendation term appearing in different enterprises.

[0012] In some embodiments, the annotation module includes: The quantity acquisition submodule is used to obtain the total number of domain name recommendation words in the second domain name recommendation words and the number of registered domain name recommendation words; The quantity determination submodule is used to determine the number of domain name recommendation words to be recommended in the second domain name recommendation words based on the total number of domain name recommendation words in the second domain name recommendation words and the number of registered domain name recommendation words.

[0013] Thirdly, a computer device is provided, comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the above method steps.

[0014] Fourthly, a computer-readable storage medium is provided, on which a computer program is stored, which, when executed by a processor, implements the above method steps.

[0015] The aforementioned domain name recommendation method, apparatus, computer equipment, and storage medium first acquire the company name information input by the user. Then, based on the company name information, initial domain name recommendation terms are retrieved from a domain name recommendation term database. Next, the initial domain name recommendation terms are annotated in multiple dimensions to obtain annotated domain name recommendation terms. Finally, domain name recommendations are made to the user based on the annotated domain name recommendation terms, thereby improving the accuracy of domain name recommendations. Attached Figure Description

[0016] Figure 1 This is an application environment diagram of the domain name recommendation method provided in the embodiments of the present invention; Figure 2 A flowchart illustrating the domain name recommendation method provided in an embodiment of the present invention; Figure 3 This is another flowchart illustrating the domain name recommendation method provided in an embodiment of the present invention; Figure 4 This is a schematic diagram of the framework of a domain name recommendation system provided in an embodiment of the present invention; Figure 5 A structural block diagram of the domain name recommendation device provided in an embodiment of the present invention; Figure 6 An internal structural diagram of a computer device provided in an embodiment of the present invention. Detailed Implementation

[0017] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0018] The domain name recommendation method provided in this application can be applied to, for example... Figure 1 In the application environment shown, terminal 102 communicates with server 104 via a network. The user inputs company name information through terminal 102. Terminal 102 sends the company name information to server 104. After receiving the company name information, server 104 first searches for initial domain name recommendations in the domain name recommendation database based on the company name information, then performs multi-dimensional annotation on the initial domain name recommendations to obtain annotated domain name recommendations, and finally recommends domain names to the user based on the annotated domain name recommendations.

[0019] The terminal 102 can be, but is not limited to, various personal computers, laptops, smartphones, tablets and portable wearable devices, and the server 104 can be implemented by a standalone server or a server cluster consisting of multiple servers.

[0020] In one embodiment, such as Figure 2 As shown, a domain name recommendation method is provided, which can be applied to... Figure 1 Taking the server in the example, the following steps are included: Step 101: Obtain the company name information entered by the user; A user interface can be displayed on the terminal to input company name information. The user enters the company name through this interface. After obtaining the company name information, the terminal triggers a query request to extract recommended domain names. This query request carries the company name information. The terminal then sends the query request to the server, which parses the request to obtain the company name information.

[0021] In some embodiments, after the server receives a query request, it can also authenticate the query request. For example, it can authenticate the query request by verifying the validity of the terminal's IP (Internet Protocol) address or by verifying the validity of the terminal's identity using a key.

[0022] Step 102: Based on the company name information, search for initial domain name recommendation keywords from the domain name recommendation keyword database; Specifically, such as Figure 3 As shown, domain name recommendations matching the company name information can be searched from the domain name recommendation database. If a matching domain name recommendation is found, the process proceeds to step 103 for multi-dimensional labeling. If no matching domain name recommendation is found, the company name information can be marked. At preset intervals, the company's brand-related basic data is retrieved again from a third-party data platform via an interface, ultimately updating the brand-related basic data of companies that were not matched in the query.

[0023] In some embodiments, prior to the step of searching for initial domain name recommendations from a domain name recommendation database based on enterprise name information, the method further includes: (A1) Obtain basic data related to the company's brand; (A2) Conduct compliance screening on the format of brand-related basic data, and select domain name recommendations that meet the format compliance requirements as the primary domain name recommendations; and (A3) Establish a domain name recommendation keyword database based on the first domain name recommendation keywords.

[0024] In some embodiments, a company's brand-related basic data includes its trade name, trademark name, website name, product name, brand name, and promotional keywords. The trade name is the full and abbreviated name of the company. The trademark name is the trademark name in the trademark database. The website name is the domain name and website name in the ICP filing data. The product name is the name of the company's products. The brand name is the company's brand. Promotional keywords are key elements such as keywords and titles from the company's website content. By extracting the company's brand-related basic data, the recommended domain names can meet the core needs of enterprise users for brand relevance.

[0025] In some embodiments, the steps for obtaining basic brand-related data of an enterprise include: (B1) Obtain basic data on the enterprise's main body, intellectual property rights, brand system, product system, and digital channels; (B2) Obtain brand-related basic data from the main body basic data, intellectual property basic data, brand system basic data, product system basic data, and digital channel basic data.

[0026] Enterprise entity basic data describes core information such as an enterprise's legal identity, organizational structure, and operational qualifications, such as... Figure 3 The enterprise registration data shown is for reference only. Intellectual property basic data records core information about the intellectual property assets owned or controlled by an enterprise, such as... Figure 3 The data shown is the company's trademark data. Basic brand system data describes information such as the company's brand architecture, core identity, and value proposition. Figure 3 The data shown is related to the company's brand. Basic product system data describes the company's core business carriers (products / services), such as product data. Basic digital channel data describes the core configuration data of the company's online business entry points (websites, applications, mini-programs, etc.), such as... Figure 3 The data shown includes the company's website information and ICP (Internet Content Provider) registration information.

[0027] The company's official website domain name can be obtained from its business registration information. Then, web crawlers can be used to extract the text information from the website's pages to obtain the company's core data, intellectual property data, brand system data, product system data, and digital channel data. In some embodiments, web crawling technology primarily utilizes HttpClient (a client-side programming toolkit) to perform domain name detection and webpage content parsing. The aforementioned basic data can also be obtained through business data cooperation and exchange, data purchase, and access to cloud platform enterprise interfaces.

[0028] The following rules can be followed to obtain data from the entity's basic data, intellectual property basic data, brand system basic data, product system basic data, and digital channel basic data, such as... Figure 3 The following basic brand-related data are shown: business name, trademark name, website name, product name, brand name, and promotional keywords: For example, the rules for generating trade names are as follows: Match company names of different company types from enterprise registration data, remove region, company type, and industry type from the company names, then perform proximity matching with the region, company type, and industry type in the company names, and combine them to generate the final trade name. The rules for generating promotional keywords are as follows: Use web crawlers to crawl the text information of company website pages, extract keywords and titles, and generate promotional keywords after preliminary screening. The rules for generating trademark names are as follows: Collect valid trademark names of company types (excluding graphic types), and generate trademark names after preliminary screening.

[0029] Suppose a company's name is AB (Beijing) Technology Co., Ltd., and the trade name could be

A BAB BA Beijing AB A Technology B Technology

AB Chinese domain name B AB official website

AB AB BA Beijing AB A Technology B Technology Chinese domain name AB official website

[0030] After obtaining the basic brand-related data, formal compliance screening rules can be set to conduct an initial screening of the data. These rules should include at least the following: 1. Truncation of the basic brand-related data according to a preset character threshold, such as truncating data exceeding 30 Chinese characters. 2. Data segmentation, dividing the data based on delimiters such as commas, semicolons, hash symbols, and spaces. 3. Removal of entirely English letters / numbers and combinations thereof, as well as filtering and deduplication of special characters. 4. Utilizing Solr's IK segmentation algorithm, based on its forward / backward maximum matching method, to further intelligently segment and extract words from basic brand-related data exceeding 3 Chinese characters in length. After formal compliance screening of the basic brand-related data using these rules, recommended keywords for the first domain name that meet the formal compliance requirements are obtained. A domain name recommended keyword database is then established to store these keywords.

[0031] ClickHouse (an open-source columnar online analytical processing database management system) can leverage its features for real-time analysis and efficient querying of massive amounts of data to establish systems such as... Figure 4 The enterprise basic information big data platform shown integrates a domain name recommendation keyword database. Furthermore, this big data platform can be used as an entry point for providing enterprise information queries.

[0032] Step 103: Perform multi-dimensional annotation on the initial domain name recommendation keywords to obtain the annotated domain name recommendation keywords; Initial domain name recommendation keywords can be labeled from multiple dimensions such as whether it is registered, category index, risk index, number of recommended keywords, shared brand extreme value, and category weight.

[0033] In some embodiments, the step of performing multi-dimensional annotation on the initial domain name recommendation terms to obtain the annotated domain name recommendation terms includes: (C1) Perform compliance screening on the content of the initial domain name recommendation keywords to obtain domain name recommendation keywords that meet the content compliance requirements as the second domain name recommendation keywords; and (C2) Perform multi-dimensional annotation on the recommended terms of the second domain name to obtain the annotated recommended terms of the domain name.

[0034] You can set content compliance filtering rules to filter the recommended keywords for the initial domain name. For example... Figure 3 As shown, the content compliance screening rules include at least the following: 1. Sensitive word filtering of the initial domain name recommendation keywords. For example, using a self-built sensitive word library to filter initial domain name recommendation keywords containing prohibited or restricted words. 2. Blacklist filtering of the initial domain name recommendation keywords. Using a self-built blacklist library to filter out initial domain name recommendation keywords containing blacklisted content. 3. Data cleaning processing of the initial domain name recommendation keywords. Specifically, custom cleaning rules can be defined, such as performing basic cleaning filtering, converting keywords shorter than 3 Chinese characters to full English, converting to corresponding traditional Chinese characters, and meeting Chinese domain name grammar rules. After the above content compliance screening, second domain name recommendation keywords that meet the content compliance requirements are obtained. Then, the second domain name recommendation keywords are annotated in multiple dimensions to obtain annotated domain name recommendation keywords.

[0035] In some embodiments, the step of performing multi-dimensional annotation on the recommended terms of the second domain name to obtain the annotated domain name recommended terms includes: (D1) Based on the registration status of the recommended keywords for the second domain name, obtain the classification index of the recommended keywords for the second domain name; (D2) Based on the classification weight and classification index of the recommended terms of the second domain name, the risk index of the recommended terms of the second domain name is obtained.

[0036] The registration status of second-domain recommended keywords is divided into those registered with domain name registry management agencies and those not registered with them, i.e., registered and unregistered. The classification index of second-domain recommended keywords can be calculated using the following formula: .

[0037] The classification weight is the proportion of each second-domain recommended term (labeled item) in the total number of second-domain recommended terms in the multi-dimensional annotation. This proportion can be set according to the importance of each second-domain recommended term. Assuming that the importance of the business name is greater than that of the trademark name among the second-domain recommended terms, as shown in Table 1 below, the classification weight for the business name can be set to 40%, and the classification weight for the trademark name to 20%. In Table 1, a certain company has 19 second-domain recommended terms annotated.

[0038] Table 1

[0039] The risk index for each second-domain recommended keyword can be calculated as: Risk Index = ∑(Classification Index) The classification weights are calculated.

[0040] In some embodiments, the step of performing multi-dimensional annotation on the recommended terms of the second domain name to obtain the annotated domain name recommended terms includes: (E1) Obtain the total frequency of the second domain name recommendation keywords appearing in different enterprises; (E2) Based on the total frequency of the second domain name recommendation in different enterprises, the common brand extreme value of the second domain name recommendation is obtained.

[0041] In some embodiments, the co-brand extreme value is the total frequency of each second domain name recommendation term appearing in multiple different enterprises. For example, if the second domain name recommendation term is trademark M, then the number of enterprises that have registered trademark M is the number of co-brand owners.

[0042] In some embodiments, the step of performing multi-dimensional annotation on the recommended terms of the second domain name to obtain the annotated domain name recommended terms includes: (F1) Get the total number of domain name recommendation terms in the second domain name recommendation terms and the number of registered domain name recommendation terms; (F2) Determine the number of domain name recommendations to be recommended in the second domain name recommendation list based on the total number of domain name recommendations in the second domain name recommendation list and the number of registered domain name recommendations.

[0043] In some embodiments, assuming the total number of domain name recommendation terms in the second domain name recommendation terms is V1 and the number of registered domain name recommendation terms is V2, then the number of domain name recommendation terms to be recommended in the second domain name recommendation terms is V = V1 - V2.

[0044] like Figure 3 As shown, the labeled domain name recommendation words can be stored in the candidate recommendation word database, which makes it easier to find the target domain name recommendation words from the candidate recommendation word database to recommend to users later.

[0045] Step 104: Recommend domain names to users based on the labeled domain name recommendation keywords.

[0046] Based on the annotation results, domain name recommendations can be given to users. As shown in Table 1, a higher risk index indicates a higher risk of the company being preemptively registered. The corresponding annotated domain name recommendations should be prioritized for users to ensure proper pre-registration protection. A higher shared brand value for an annotated domain name recommendation indicates that the domain name is held by multiple companies. Domain name recommendations with a high shared brand value should be prioritized for user recommendations, and therefore, they should be recommended earlier. As shown in Table 2, domain name recommendations can be prioritized by ranking domain name recommendations by risk index (highest) and number of shared brands (decreasing), and then by marking them with a "firepower value."

[0047] Table 2

[0048] The system can profile the recommended domain names after labeling them and return the data to end users in various forms, including reports, online browsing, and API (Application Programming Interface). Specifically, it can profile all the recommended domain names associated with each enterprise entity and then return the results to end users.

[0049] The above-mentioned domain name recommendation process does not require uploading electronic materials or filling out complicated online forms. It can achieve the recommendation of domain name recommendation words when submitting the registration, and the recommendation process is simple.

[0050] In the domain name recommendation method described above, the user-inputted company name information is first obtained. Then, based on the company name information, initial domain name recommendation terms are retrieved from the domain name recommendation term database. Next, these initial domain name recommendation terms are annotated in multiple dimensions to obtain annotated domain name recommendation terms. Finally, domain name recommendations are made to the user based on these annotated domain name recommendation terms, thus improving the accuracy of domain name recommendations.

[0051] It should be understood that, although Figure 2-3 The steps in the flowchart are shown sequentially as indicated by the arrows, but these steps are not necessarily executed in the order indicated by the arrows. Unless otherwise specified herein, there is no strict order in which these steps are executed, and they can be performed in other orders. Figure 2-3At least some of the steps in the process may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these sub-steps or stages is not necessarily sequential, but can be executed in turn or alternately with other steps or at least some of the sub-steps or stages of other steps.

[0052] In one embodiment, such as Figure 5 As shown, a domain name recommendation device 20 is provided, including: an information acquisition module 201, a search module 202, an annotation module 203, and a recommendation module 204, wherein: the information acquisition module 201 is used to acquire enterprise name information input by the user; the search module 202 is used to search for initial domain name recommendation words from the domain name recommendation word database based on the enterprise name information; the annotation module 203 is used to perform multi-dimensional annotation on the initial domain name recommendation words to obtain annotated domain name recommendation words; and the recommendation module 204 is used to recommend domain names to the user based on the annotated domain name recommendation words.

[0053] In some embodiments, the domain name recommendation device 20 further includes: a basic data acquisition module, a formal compliance screening module, and a database establishment module. The basic data acquisition module acquires brand-related basic data of the enterprise; the formal compliance screening module performs formal compliance screening on the brand-related basic data to obtain domain name recommendation terms that meet formal compliance requirements as first domain name recommendation terms; and the database establishment module establishes the domain name recommendation term database based on the first domain name recommendation terms.

[0054] In some embodiments, the basic data acquisition module includes a first acquisition submodule and a second acquisition submodule. The first acquisition submodule is used to acquire the enterprise's main basic data, intellectual property basic data, brand system basic data, product system basic data, and digital channel basic data; and the second acquisition submodule is used to acquire brand-related basic data from the main basic data, the intellectual property basic data, the brand system basic data, the product system basic data, and the digital channel basic data.

[0055] In some embodiments, the annotation module 203 includes a content compliance screening submodule and a multi-dimensional annotation submodule. The content compliance screening submodule is used to screen the content of the initial domain name recommendation terms for compliance, obtaining domain name recommendation terms that meet content compliance requirements as second domain name recommendation terms; and the multi-dimensional annotation submodule is used to perform multi-dimensional annotation on the second domain name recommendation terms, obtaining annotated domain name recommendation terms.

[0056] In some embodiments, the annotation module 203 includes a classification index acquisition submodule and a risk index acquisition submodule. The classification index acquisition submodule is used to obtain the classification index of the second domain name recommendation term based on its registration status; the risk index acquisition submodule is used to obtain the risk index of the second domain name recommendation term based on its classification weight and the classification index.

[0057] In some embodiments, the annotation module 203 includes a frequency acquisition submodule and an extreme value acquisition submodule. The frequency acquisition submodule is used to acquire the total frequency of the second domain name recommendation term appearing in different enterprises; the extreme value acquisition submodule is used to obtain the common brand extreme value of the second domain name recommendation term based on the total frequency of the second domain name recommendation term appearing in different enterprises.

[0058] In some embodiments, the annotation module 203 includes a quantity acquisition submodule and a quantity determination submodule. The quantity acquisition submodule is used to acquire the total number of domain name recommendation terms in the second domain name recommendation terms and the number of registered domain name recommendation terms; the quantity determination submodule is used to determine the number of domain name recommendation terms to be recommended in the second domain name recommendation terms based on the total number of domain name recommendation terms in the second domain name recommendation terms and the number of registered domain name recommendation terms.

[0059] For specific limitations regarding the domain name recommendation device, please refer to the limitations of the domain name recommendation method above, which will not be repeated here. Each module in the aforementioned domain name recommendation device can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in a computer device in hardware form, or stored in the memory of a computer device in software form, so that the processor can call and execute the corresponding operations of each module.

[0060] In one embodiment, a computer device is provided, which may be a server, and its internal structure diagram may be as follows: Figure 6 As shown, the computer device includes a processor, memory, network interface, and database connected via a system bus. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system, computer programs, and database. The internal memory provides an environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The database stores data. The network interface communicates with external terminals via a network connection. When executed by the processor, the computer program implements a domain name recommendation method.

[0061] Those skilled in the art will understand that Figure 6The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0062] In one embodiment, a computer device is provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the above method steps.

[0063] In one embodiment, a computer-readable storage medium is provided having a computer program stored thereon, which, when executed by a processor, implements the above method steps.

[0064] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the embodiments of the above methods. Any references to memory, storage, databases, or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), dual data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), RAMbus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

[0065] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0066] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the invention patent. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this patent application should be determined by the appended claims.

Claims

1. A domain name recommendation method, characterized in that, The method includes: Obtain the company name information entered by the user; Based on the company name information, search for initial domain name recommendation keywords from the domain name recommendation keyword database; The initial domain name recommendation terms are annotated in multiple dimensions to obtain annotated domain name recommendation terms; and Based on the domain name recommendation keywords that have been annotated, domain names are recommended to the user.

2. The domain name recommendation method according to claim 1, characterized in that, Before the step of searching for initial domain name recommendations from the domain name recommendation database based on the enterprise name information, the method also includes: Obtain basic brand-related data from the enterprise; The format of the aforementioned brand-related basic data is screened for compliance, and domain name recommendations that meet the format compliance requirements are selected as the first domain name recommendations; and Based on the first domain name recommendation terms, establish the domain name recommendation term database.

3. The domain name recommendation method according to claim 2, characterized in that, The steps for obtaining basic brand-related data of an enterprise include: Acquire basic enterprise data, including core business data, intellectual property data, brand system data, product system data, and digital channel data; and The brand-related basic data is obtained from the main body basic data, the intellectual property basic data, the brand system basic data, the product system basic data, and the digital channel basic data.

4. The domain name recommendation method according to claim 1, characterized in that, The step of performing multi-dimensional annotation on the initial domain name recommendation terms to obtain the annotated domain name recommendation terms includes: The initial domain name recommendation keywords are subjected to compliance screening to obtain domain name recommendation keywords that meet the content compliance requirements, which are then used as the second domain name recommendation keywords; and The recommended keywords for the second domain name are annotated in multiple dimensions to obtain the annotated recommended keywords for the domain name.

5. The domain name recommendation method according to claim 1, characterized in that, The step of performing multi-dimensional annotation on the recommended terms of the second domain name to obtain the annotated recommended terms includes: Based on the registration status of the recommended keywords for the second domain name, the classification index of the recommended keywords for the second domain name is obtained; The risk index of the recommended terms for the second domain name is obtained based on the category weight and the category index.

6. The domain name recommendation method according to claim 1, characterized in that, The step of performing multi-dimensional annotation on the recommended terms of the second domain name to obtain the annotated recommended terms includes: Obtain the total frequency of the recommended keywords for the second domain name appearing in different enterprises; The common brand extreme value of the second domain name recommendation term is obtained by calculating the total frequency of its appearance in different enterprises.

7. The domain name recommendation method according to claim 1, characterized in that, The step of performing multi-dimensional annotation on the recommended terms of the second domain name to obtain the annotated recommended terms includes: Obtain the total number of domain name recommendation terms in the second domain name recommendation terms and the number of registered domain name recommendation terms; Based on the total number of domain name recommendation terms in the second domain name recommendation terminology and the number of registered domain name recommendation terms, the number of domain name recommendation terms to be recommended in the second domain name recommendation terminology is determined.

8. A domain name recommendation device, characterized in that, The device includes: The information acquisition module is used to acquire the company name information input by the user; The search module is used to search for initial domain name recommendations from the domain name recommendation keyword database based on the company name information; The annotation module is used to perform multi-dimensional annotation on the initial domain name recommendation terms to obtain annotated domain name recommendation terms; and The recommendation module is used to recommend domain names to users based on the domain name recommendation keywords marked on the domain name.

9. A computer device, comprising: A memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that the processor, when executing the computer program, implements the steps of the method according to any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 7.

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