Keyword analysis and processing method and system based on search engine optimization (SEO)

Abstract

The invention relates to the field of the Internet, and discloses a keyword analysis and processing method and system based on search engine optimization (SEO), comprising: obtaining a keyword library of a target website webpage; obtaining the current exposure of the target website webpage under the initial keyword library, and when the current exposure is lower than a set exposure standard, inputting core keywords into a search engine for searching, obtaining a complete phrase set automatically provided by a search bar and a plurality of competing website webpages; obtaining the keyword libraries of all competing website webpages, and performing keyword blank analysis according to the complete phrase set and the keyword library of the competing website webpages, to obtain a plurality of blank long-tail keywords; determining the best keyword combination in the keyword library of the target website webpage, and expanding the keyword library of the target website webpage according to the best keyword combination; the invention can help small and medium-sized or start-up enterprise websites avoid direct competition with large brands, and improve the exposure rate of enterprise websites.

Description

Technical Field

[0001] The present invention relates to the field of Internet, and more specifically, to a keyword analysis and processing method and system based on search engine optimization (SEO). Background Art

[0002] In today's digital age, the Internet has become the main channel for information dissemination, and the popularity of search engines enables users to quickly obtain the information they need. For companies and websites, improving visibility in search engine results has become an important business strategy. Search engine optimization (SEO), as the core means to achieve this goal, emphasizes improving the website's search ranking and traffic by optimizing keyword selection and content quality.

[0003] However, for small and medium-sized websites, some major core keywords are usually occupied by large brand companies; these brands, relying on their strong resource advantages, are able to comprehensively optimize and promote highly competitive core keywords; therefore, when users search for these core keywords, the search results often give priority to the websites or web pages of these large brands; and for small and medium-sized websites or emerging websites with limited resources, choosing core keywords is difficult to bring effective competitive advantages, and may even make it difficult for them to obtain a high ranking in the search results; for this reason, small and medium-sized websites usually turn their attention to long-tail keywords, hoping to attract accurate users through more segmented and specific keyword combinations, thereby gaining more display opportunities in user searches with specific needs; however, the existing long-tail keyword selection process usually relies on the experience and judgment of technical personnel and lacks effective analysis methods; this experience-based selection method often leads to the selected long-tail keywords having low search volume or unclear demand, which ultimately leads to unsatisfactory exposure and traffic improvement effects for small and medium-sized websites. Summary of the invention

[0004] In order to overcome the above-mentioned defects of the prior art, an embodiment of the present invention provides a keyword analysis and processing method and system based on search engine optimization (SEO).

[0005] To achieve the above object, the present invention provides the following technical solutions:

[0006] In a first aspect, a keyword analysis and processing method based on search engine optimization (SEO) is provided, the method comprising:

[0007] Obtain a keyword library of a target website webpage, wherein the keyword library includes a core keyword and multiple long-tail keywords;

[0008] Obtain the current exposure of the target website webpage under the initial keyword library, and when the current exposure is lower than the set exposure standard, input the core keyword into the search engine for search, and obtain the completion phrase set automatically provided by the search bar and multiple competing website webpages, wherein the completion phrase set includes Z completion phrases, where Z is an integer greater than zero;

[0009] Obtain the keyword library of all competing website pages, and perform keyword blank analysis based on the completion phrase set and the keyword library of competing website pages to obtain multiple blank long-tail keywords;

[0010] The preconfigured genetic algorithm is used to determine the best keyword combination in the keyword library of the target website webpage, the keyword library of the target website webpage is expanded according to the best keyword combination, and the expanded keyword library of the target website webpage is used for search engine optimization.

[0011] Furthermore, the method for obtaining long-tail keywords in the keyword library of the target website is as follows:

[0012] a1: Obtain the target website webpage, obtain the text in the target website webpage, perform word segmentation on the text, remove stop words, and perform indexing to obtain a word set containing multiple independent words, each of which is associated with a unique index number;

[0013] a2: Obtain independent words that are core keywords in the word set, and use independent words that are not core keywords in the word set as candidate words, and obtain the word frequency of each candidate word in the text;

[0014] a3: Compare the word frequency of each candidate word with the preset first word frequency threshold. If the word frequency is greater than or equal to the preset first word frequency threshold, the corresponding candidate word is marked as an alternative word, and Q alternative words are obtained; if the word frequency is less than the preset first word frequency threshold, the corresponding candidate word is not marked as an alternative word, and Q is an integer greater than zero;

[0015] a4: extract the vth candidate word from the Q candidate words, and calculate the minimum character spacing between the vth candidate word and the core keyword according to the index number, where v is an integer greater than zero;

[0016] a5: Get the set spacing threshold d, determine whether the minimum character spacing of the vth candidate word is less than or equal to the set spacing threshold d, if so, combine the candidate word corresponding to the minimum character spacing with the core keyword to obtain a combined word, and obtain the word frequency of the combined word in the text; if not, set v=v+1, and return to step a4, d is an integer greater than zero, and the value of d is 1;

[0017] a6: Get the second word frequency threshold, compare the word frequency of the combination word with the second word frequency threshold, if the word frequency of the combination word is greater than or equal to the second word frequency threshold, mark the combination word as a long-tail keyword in the keyword library of the target website webpage, set v=v+1, and return to step a4; if the word frequency of the combination word is less than the second word frequency threshold, set v=v+1, and return to step a4;

[0018] a7: Repeat the above steps a4 to a6 until v=Q and the loop ends, obtaining multiple long-tail keywords belonging to the keyword library of the target website.

[0019] Furthermore, the step of obtaining the current exposure of the target website webpage under the initial keyword library includes:

[0020] Obtain exposure data of core keywords in the keyword library of the target website's webpage, as well as exposure data of each long-tail keyword; the exposure data includes search volume, ranking in search results, and click-through rate;

[0021] The exposure data of core keywords and long-tail keywords are input into the pre-built exposure calculation model to obtain the current exposure of the target website web page under the initial keyword library.

[0022] Furthermore, the expression of the pre-built exposure calculation model is as follows:

[0023] ;

[0024] Where: is the current exposure, The search volume of the core keywords. The ranking of core keywords in search results. The click-through rate of the core keywords. is the search volume of the i-th long-tail keyword, is the ranking of the i-th long-tail keyword in the search results, is the click-through rate of the i-th long-tail keyword.

[0025] Furthermore, the keyword gap analysis is performed based on the completion phrase set and the keyword library of the competing website webpage, including:

[0026] b1: Get the jth completion phrase in the completion phrase set, and perform word segmentation on the jth completion phrase, remove stop words and core keywords, and obtain the completion word, where j is an integer greater than zero;

[0027] b2: Extract multiple long-tail keywords from the keyword library of all competing websites, perform word segmentation on all long-tail keywords, remove stop words and core keywords, and obtain multiple feature words;

[0028] b3: Calculate the similarity between the completion word and each feature word to obtain multiple similarities, sort the multiple similarities from large to small according to the numerical value, compare the first-ranked similarity with the preset similarity threshold, if the first-ranked similarity is greater than or equal to the preset similarity threshold, then mark the jth completion phrase to which the corresponding completion word belongs as a blank long-tail keyword, and set j=j+1, and return to step b1; if the first-ranked similarity is less than the preset similarity threshold, then set j=j+1, and return to step b1;

[0029] b4: Repeat the above steps b1 to b3 until j=Z, ending the loop and obtaining multiple blank long-tail keywords.

[0030] Further, the keyword combination includes the core keyword of the target website webpage, a plurality of long-tail keywords and at least one blank keyword;

[0031] The method of using a preconfigured genetic algorithm to determine the best keyword combination in the keyword library of the target website webpage includes:

[0032] c1: Initialize the population: randomly generate an original population based on multiple blank long-tail keywords, wherein the original population contains E individuals, each of which represents a keyword combination, and E is an integer greater than zero;

[0033] c2: Fitness evaluation: Under each individual, pre-adjust the keyword library of the target website webpage to obtain the adjusted keyword library of the target website webpage, obtain the exposure of the target website webpage under the adjusted keyword library, input the exposure of the target website webpage under the adjusted keyword library into the pre-built fitness function, and calculate the fitness of each individual;

[0034] c3: Selection: Use the roulette method to select two individuals with high fitness in the original population as the father and mother;

[0035] c4: Crossover: Perform crossover operation on the father and mother to produce new individuals;

[0036] c5: mutation: perform mutation operation on the new individuals to obtain X new individuals, combine the X new individuals into a new population, replace the original population with the new population, and return to step c2;

[0037] c6: Repeat steps c2 to c5 above until the fitness of individuals in the original population or the new population is greater than or equal to the preset fitness threshold, or the number of iterations is greater than or equal to the preset maximum number of iterations threshold, and output the keyword combination represented by the corresponding individual as the best keyword combination.

[0038] Furthermore, the calculation formula of the pre-constructed fitness function is: ; Where: For fitness, is a natural constant, To increase the exposure of the target website pages under the adjusted keyword library.

[0039] In a second aspect, a keyword analysis and processing system based on search engine optimization (SEO) is provided, comprising:

[0040] A data acquisition module is used to acquire a keyword library of a target website webpage, wherein the keyword library includes a core keyword and multiple long-tail keywords;

[0041] An exposure judgment module is used to obtain the current exposure of the target website webpage under the initial keyword library, and when the current exposure is lower than the set exposure standard, input the core keyword into the search engine for search, and obtain the completion phrase set automatically provided by the search bar and multiple competing website webpages, wherein the completion phrase set includes Z completion phrases, where Z is an integer greater than zero;

[0042] A blank analysis module is used to obtain the keyword library of all competing website pages, and perform keyword blank analysis based on the completion phrase set and the keyword library of the competing website pages to obtain multiple blank long-tail keywords;

[0043] The SEO optimization module is used to determine the best keyword combination in the keyword library of the target website web page using a preconfigured genetic algorithm, expand the keyword library of the target website web page according to the best keyword combination, and use the expanded keyword library of the target website web page to perform search engine optimization.

[0044] In a third aspect, an electronic device is provided, comprising a memory, a processor, and a computer program stored in the memory and running on the processor, wherein when the processor executes the computer program, the keyword analysis and processing method based on search engine optimization (SEO) as described above is implemented.

[0045] In a fourth aspect, a computer-readable storage medium is provided, on which a computer program is stored, and when the computer program is executed, any of the above-mentioned keyword analysis and processing methods based on search engine optimization (SEO) is implemented.

[0046] Compared with the prior art, the present invention has the following beneficial effects:

[0047] The present application discloses a keyword analysis and processing method and system based on search engine optimization (SEO), including: obtaining a keyword library of a target website webpage; obtaining the current exposure of the target website webpage under the initial keyword library, and when the current exposure is lower than the set exposure standard, inputting the core keyword into the search engine for search, obtaining a complete phrase set automatically provided by the search bar and a plurality of competing website webpages; obtaining a keyword library of all competing website webpages, and performing keyword blank analysis based on the complete phrase set and the keyword library of the competing website webpage to obtain a plurality of blank long-tail keywords; determining the best keyword combination in the keyword library of the target website webpage, and expanding the keyword library of the target website webpage according to the best keyword combination; based on the above content, the present invention is aimed at In order to solve the problem that small and medium-sized websites are at a disadvantage in the competition for core keywords, an intelligent optimization solution based on phrase completion and gap analysis of keyword libraries of competing websites is provided; by identifying uncovered long-tail keywords and using genetic algorithms to optimize keyword combinations, the present invention effectively avoids direct competition with large brands on core keywords; its automated keyword selection process solves the problem of relying on the experience of technical personnel and low word selection accuracy in existing methods, thereby helping small and medium-sized websites to increase exposure and traffic in user searches for specific needs and achieve accurate traffic diversion; compared with the traditional experience-based approach, the present invention ensures the scientific expansion of the keyword library and the sustainability of search engine optimization effects, truly enhancing the competitiveness of websites with limited resources in search results. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] Figure 1 A flowchart of a keyword analysis and processing method based on search engine optimization (SEO) provided by the present invention;

[0049] Figure 2 A module structure diagram of a keyword analysis and processing system based on search engine optimization (SEO) provided by the present invention;

[0050] Figure 3 The present invention provides a schematic structural diagram of an electronic device. DETAILED DESCRIPTION

[0051] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0052] Example 1

[0053] See also Figure 1As shown, this embodiment discloses a keyword analysis and processing method based on search engine optimization (SEO), the method comprising:

[0054] S101: Obtain a keyword library of a target website webpage, wherein the keyword library includes a core keyword and multiple long-tail keywords;

[0055] It should be understood that: the target website refers to the website that the user has determined to be in need of search engine optimization (SEO), such as a website that sells running shoes; and the keyword library is a list of all keywords collected by a target website or a target website webpage; these keywords are words or phrases that the target website or the target website webpage hopes that users can find the website or webpage when searching, and their role is to help the website be better found by search engines and users, improve the website's ranking in search results, and increase the website's exposure and visibility; but some major core keywords are usually occupied by large brand enterprise websites. In other words, when users search for these core keywords, the websites or webpages of these large brand enterprises usually appear or rank high, because these brands have more resources to optimize and promote these highly competitive keywords, which is very important for For small and medium-sized websites or emerging websites, if core keywords are used as keywords for their own websites, the competitiveness of the websites will be relatively low, and it will be difficult to obtain a high ranking in search engines; this makes small and medium-sized websites usually choose to focus on long-tail keywords. However, the selection of long-tail keywords for existing websites usually relies on the experience of technical personnel and lacks effective analysis. This results in that although small and medium-sized websites can attract more accurate users through long-tail keywords, the exposure of small and medium-sized websites or web pages is not satisfactory due to defects such as low search volume of the designed or selected long-tail keywords; therefore, the challenge faced by small and medium-sized websites is how to select and optimize appropriate long-tail keywords to ensure that they can avoid direct competition with large brands and achieve sufficient traffic growth. The present invention is a solution proposed under this technical problem;

[0056] The core keywords in the keyword library of the target website webpage are manually input by the technicians according to the actual situation or the title of the target website webpage;

[0057] Among them, the method for obtaining long-tail keywords in the keyword library of the target website is as follows:

[0058] a1: Obtain the target website webpage, obtain the text in the target website webpage, perform word segmentation on the text, remove stop words, and perform indexing to obtain a word set containing multiple independent words, each of which is associated with a unique index number;

[0059] Among them, the text in the target website web page can be obtained by using OCR technology or directly extracting the text content from the HTML text of the target website web page; the OCR (Optical Character Recognition) technology is a technology used to extract text from images or scanned documents; it can convert the text information in the image into editable electronic text, so it is widely used in fields such as document digitization, character recognition, and information processing. Its specific implementation tools include but are not limited to Tesseract OCR or Adobe Acrobat OCR, etc.;

[0060] It should be noted that: the word segmentation of the text is realized by using existing word segmentation tools, such as the jieba word segmentation tool, etc. The stop words include but are not limited to "ah", "ma", and "le", etc.; in addition, the index numbers are numbered once in the order of the independent words. For example, if the text in the target website web page is "A selection scheme for a comfortable anti-odor running shoe", after word segmentation, it gets "A / comfortable / anti-odor / running shoe / of / selection / scheme". Assuming that "running shoe" is the core keyword, if the index numbers are numbered in order, the index number of "running shoe" is 4;

[0061] a2: Obtain the independent words in the word set that are core keywords, use the independent words in the word set that are not core keywords as candidate words, and obtain the word frequency of each candidate word in the text;

[0062] a3: Compare the word frequency of each candidate word with a preset first word frequency threshold. If the word frequency is greater than or equal to the preset first word frequency threshold, mark the corresponding candidate word as an alternative word to obtain Q alternative words; if the word frequency is less than the preset first word frequency threshold, do not mark the corresponding candidate word as an alternative word. Q is an integer greater than zero;

[0063] a4: Extract the v-th alternative word among the Q alternative words, and calculate the minimum character distance between the v-th alternative word and the core keyword according to the index number. v is an integer greater than zero;

[0064] Among them, the character distance is calculated according to the absolute difference between the index numbers of the core keyword and the alternative word, and the minimum character distance is obtained according to the sorting of the character distances. Further explanation is that the multiple character distances are sorted from smallest to largest in value, and the character distance corresponding to the first in the sorting is used as the minimum character distance;

[0065] For example, there is a core keyword "B1" and an alternative word "A1", and "A1" appears multiple times in the text. Then, the character distance between each occurrence of "A1" and "B1" is calculated to obtain multiple character distances. The multiple character distances are sorted in ascending order according to the numerical value, and the character spacing corresponding to the first one in the sorting is taken as the minimum character spacing;

[0066] a5: Get the set spacing threshold d, determine whether the minimum character spacing of the vth candidate word is less than or equal to the set spacing threshold d, if so, combine the candidate word corresponding to the minimum character spacing with the core keyword to obtain a combined word, and obtain the word frequency of the combined word in the text; if not, set v=v+1, and return to step a4, d is an integer greater than zero, and the value of d is 1;

[0067] a6: Get the second word frequency threshold, compare the word frequency of the combination word with the second word frequency threshold, if the word frequency of the combination word is greater than or equal to the second word frequency threshold, mark the combination word as a long-tail keyword in the keyword library of the target website webpage, set v=v+1, and return to step a4; if the word frequency of the combination word is less than the second word frequency threshold, set v=v+1, and return to step a4;

[0068] a7: Repeat the above steps a4 to a6 until v=Q, and end the loop to obtain multiple long-tail keywords belonging to the keyword library of the target website;

[0069] For example, suppose that after word segmentation, the text in the target website webpage is “a kind of comfortable anti-odor running shoes that can also play a certain anti-slip role on rainy days…”, and since “running shoes” is the core keyword, the independent words other than “running shoes” are taken as candidate words, and after counting the word frequency of each candidate word, it is found that the word frequencies of the two candidate words “anti-slip” and “anti-odor” are greater than or equal to the preset first word frequency threshold, then “anti-slip” and “anti-odor” are taken as candidate words, and then the frequency of “anti-slip”, “anti-odor” and “running shoes” are calculated respectively. After counting the character spacing of "anti-odor" and "shoes", it is found that the nearest character spacing between "anti-odor" and "running shoes" is 1, and is less than the set spacing threshold d. Therefore, "anti-odor" and "running shoes" are combined to obtain the combined word "anti-odor running shoes". Further, after counting the word frequency of "anti-odor running shoes", it is found that it is greater than or equal to the second word frequency threshold, and it is used as a long-tail keyword in the keyword library of the target website webpage; and because the character spacing between "anti-slip" and "running shoes" is greater than the set spacing threshold d, "anti-slip" and "running shoes" are not combined, nor are they considered as a long-tail keyword;

[0070] The above-mentioned semi-automatic method can help technical personnel to quickly extract the keyword library of the target website's web pages, thereby providing technical support for the subsequent optimization of the keyword library of the target website.

[0071] S102: obtaining the current exposure of the target website webpage under the initial keyword library, and when the current exposure is lower than the set exposure standard, inputting the core keyword into the search engine for searching, obtaining a set of completion phrases automatically provided by the search bar and a plurality of competing website webpages, wherein the set of completion phrases includes Z completion phrases, where Z is an integer greater than zero;

[0072] Specifically, obtaining the current exposure of the target website webpage under the initial keyword library includes:

[0073] Obtain exposure data of core keywords in the keyword library of the target website's webpage, as well as exposure data of each long-tail keyword; the exposure data includes search volume, ranking in search results, and click-through rate, and the search volume, ranking in search results, and click-through rate are unitless values;

[0074] Input the exposure data of core keywords and long-tail keywords into the pre-built exposure calculation model to obtain the current exposure of the target website webpage under the initial keyword library;

[0075] The expression of the pre-built exposure calculation model is as follows:

[0076] ;

[0077] Where: is the current exposure, The search volume of the core keywords. The ranking of core keywords in search results. The click-through rate of the core keywords. is the search volume of the i-th long-tail keyword, is the ranking of the i-th long-tail keyword in the search results, is the click-through rate of the i-th long-tail keyword;

[0078] It should be noted that: the set exposure standard is an exposure threshold preset by a technician. If the current exposure is less than the preset exposure threshold, it is determined that the current exposure is lower than the set exposure standard. On the contrary, if the current exposure is greater than or equal to the preset exposure threshold, it is determined that the current exposure is higher than the set exposure standard. It should be further noted that if the current exposure is lower than the set exposure standard, it means that the access popularity or traffic of the target website webpage under the initial keyword library is lower than the expected index or set target, and keyword intervention should be performed to ensure the access activity of the target website.

[0079] It should also be noted that after entering the core keywords into the search engine for search, the search engine will feedback to multiple competing websites of the same type as the target website, and these competing websites can often obtain good exposure through the core keywords. This is because most of these competing websites are large brand companies, which have obtained most of the traffic of the core keywords through methods such as traffic investment. Therefore, if small and medium-sized enterprises or start-ups continue to use core keywords for search engine optimization, it is often difficult to compete with competing websites and improve visibility;

[0080] It is worth noting that: the acquisition of multiple competing website web pages is determined according to preset screening conditions, and the preset screening conditions include a predetermined quantity condition and a predetermined ranking condition. For example, if the predetermined quantity condition is 10 and the predetermined ranking condition is the top 10, then 10 competing website web pages fed back by the search engine are acquired;

[0081] It should be understood that the set of completion phrases automatically provided by the search bar means that when a user enters a keyword in the search box of the search engine, the search engine will provide a series of related phrase suggestions in real time based on the user's input. These completion phrases are based on the search engine's data or existing algorithms, and reflect search trends and hot topics. For example, when a user searches in a search engine and enters the word "running shoes", the search bar will automatically provide "breathable and anti-odor running shoes" or "comfortable running shoes", etc. Usually these completion phrases are dynamically updated according to real-time hot spots. Comparing these dynamically updated completion phrases with the popular long-tail keywords of competing websites can help identify optimization opportunities for long-tail keywords in the target website's initial keyword library.

[0082] S103: Obtain a keyword library of all competing website pages, and perform keyword blank analysis based on the completion phrase set and the keyword library of the competing website pages to obtain a plurality of blank long-tail keywords;

[0083] Among them, the keyword library of the competing website's webpage contains a core keyword and multiple long-tail keywords;

[0084] It should be noted that: since the web pages of competing websites are retrieved based on core keywords, the core keywords of all competing website pages are the same as those of the target website pages. The difference is that the long-tail keywords of the competing website pages may be different from those of the target website pages. Therefore, through keyword blank analysis, these blank long-tail keywords can be effectively obtained;

[0085] It should be further explained that: the keyword library of all competing website pages is the same as the acquisition logic of the keyword library of the target website page, and the details are referred to the above related parts, which will not be repeated here;

[0086] In implementation, the keyword gap analysis is performed based on the completion phrase set and the keyword library of the competing website webpage, including:

[0087] b1: Get the jth completion phrase in the completion phrase set, and perform word segmentation on the jth completion phrase, remove stop words and core keywords, and obtain the completion word, where j is an integer greater than zero;

[0088] b2: Extract multiple long-tail keywords from the keyword library of all competing websites, perform word segmentation on all long-tail keywords, remove stop words and core keywords, and obtain multiple feature words;

[0089] b3: Calculate the similarity between the completion word and each feature word to obtain multiple similarities, sort the multiple similarities from large to small according to the numerical value, compare the first-ranked similarity with the preset similarity threshold, if the first-ranked similarity is greater than or equal to the preset similarity threshold, then mark the jth completion phrase to which the corresponding completion word belongs as a blank long-tail keyword, and set j=j+1, and return to step b1; if the first-ranked similarity is less than the preset similarity threshold, then set j=j+1, and return to step b1;

[0090] It should be noted that: the similarity between the completion word and each feature word is calculated by the cosine similarity formula;

[0091] b4: Repeat the above steps b1 to b3 until j=Z, and the loop ends to obtain multiple blank long-tail keywords;

[0092] It should also be noted that blank long-tail keywords refer to long-tail keywords that are not covered by competing websites. Compared with core keywords, blank long-tail keywords have lower competitiveness, but still have certain traffic potential and competitiveness, suitable for attracting niche markets or meeting specific user needs. By effectively identifying the "keyword gaps" of competing websites, it is beneficial to quickly obtain traffic and increase exposure at the current moment, and can help small and medium-sized or start-up websites avoid direct competition with large brands, focus on the specific needs of the niche market, and achieve accurate traffic diversion. With the help of these blank long-tail keywords, small and medium-sized websites can quickly establish popularity among target users, obtain natural search traffic in specific scenarios, and improve their rankings in search engines, thereby increasing the exposure of target websites in specific niches.

[0093] S104: using a preconfigured genetic algorithm to determine the best keyword combination in the keyword library of the target website webpage, expanding the keyword library of the target website webpage according to the best keyword combination, and using the expanded keyword library of the target website webpage to perform search engine optimization;

[0094] Wherein, the keyword combination includes the core keyword of the target website webpage, multiple long-tail keywords and at least one blank keyword;

[0095] In implementation, the method of using a preconfigured genetic algorithm to determine the best keyword combination in the keyword library of a target website webpage includes:

[0096] c1: Initialize the population: randomly generate an original population based on multiple blank long-tail keywords, wherein the original population contains E individuals, each of which represents a keyword combination, and E is an integer greater than zero;

[0097] c2: Fitness evaluation: Under each individual (i.e., each keyword combination in the original population), pre-adjust the keyword library of the target website webpage to obtain the adjusted keyword library of the target website webpage, obtain the exposure of the target website webpage under the adjusted keyword library, input the exposure of the target website webpage under the adjusted keyword library into the pre-built fitness function, and calculate the fitness of each individual;

[0098] It should be noted that: in each individual (i.e., each keyword combination in the original population), pre-adjusting the keyword library of the target website webpage means adding at least one blank long-tail keyword to the initial keyword library of the target website webpage, thereby obtaining the adjusted keyword library of the target website webpage;

[0099] It should also be noted that the logic for obtaining the exposure of the target website webpage under the adjusted keyword library is the same as the above-mentioned logic for obtaining the current exposure of the target website webpage under the initial keyword library. For details, please refer to the above-mentioned description of the relevant part, and no further elaboration is given here.

[0100] The calculation formula of the pre-constructed fitness function is: ; Where: For fitness, is a natural constant, To adjust the exposure of the target website pages under the keyword library;

[0101] c3: Selection: Use the roulette method to select two individuals with high fitness in the original population as the father and mother;

[0102] c4: Crossover: Perform crossover operation on the father and mother to produce new individuals;

[0103] It should be noted that the crossover operation on the paternal parent and the maternal parent is implemented based on a crossover operation, and the crossover operation includes but is not limited to one of a single-point crossover, a uniform crossover or a sequential crossover, etc.;

[0104] c5: mutation: perform mutation operation on the new individuals to obtain X new individuals, combine the X new individuals into a new population, replace the original population with the new population, and return to step c2;

[0105] In genetic algorithms, mutation operations are used to introduce genetic diversity and prevent the algorithm from falling into a local optimum. The mutation operation on new individuals is implemented by uniform mutation or Gaussian mutation.

[0106] c6: Repeat the above steps c2 to c5 until the fitness of the individuals in the original population or the new population is greater than or equal to the preset fitness threshold, or the number of iterations is greater than or equal to the preset maximum number of iterations threshold, and output the keyword combination represented by the corresponding individual as the best keyword combination;

[0107] For example: Assume that the maximum number of iterations is 100 times, and record the individual with the highest fitness in the current population and its fitness value after each iteration; if it is found that the fitness value has not changed significantly in a certain generation, it is considered that the convergence condition is met, the iteration is stopped, and the keyword combination represented by the corresponding individual is output as the best keyword combination.

[0108] Example 2

[0109] See also Figure 2 As shown, based on the same inventive concept, this embodiment discloses a keyword analysis and processing system based on search engine optimization SEO. For details not provided in this embodiment, please refer to the relevant parts of the above embodiment 1. The system includes:

[0110] The data acquisition module 210 is used to acquire a keyword library of a target website webpage, wherein the keyword library includes a core keyword and a plurality of long-tail keywords;

[0111] The exposure judgment module 220 is used to obtain the current exposure of the target website webpage under the initial keyword library, and when the current exposure is lower than the set exposure standard, input the core keyword into the search engine for search, and obtain the completion phrase set automatically provided by the search bar and multiple competing website webpages, wherein the completion phrase set includes Z completion phrases, where Z is an integer greater than zero;

[0112] A blank analysis module 230 is used to obtain a keyword library of all competing website pages, and perform keyword blank analysis based on the completion phrase set and the keyword library of the competing website pages to obtain a plurality of blank long-tail keywords;

[0113] The SEO optimization module 240 is used to determine the best keyword combination in the keyword library of the target website webpage using a preconfigured genetic algorithm, expand the keyword library of the target website webpage according to the best keyword combination, and use the expanded keyword library of the target website webpage to perform search engine optimization.

[0114] Example 3

[0115] See also Figure 3 As shown, this embodiment discloses an electronic device, including a memory, a processor, and a computer program stored in the memory and running on the processor, and when the processor executes the computer program, it implements the keyword analysis and processing method based on search engine optimization SEO provided by any one of the above methods.

[0116] Since the electronic device introduced in the content of this embodiment is an electronic device used to implement a keyword analysis and processing method based on search engine optimization SEO in the embodiment of this application, based on the keyword analysis and processing method based on search engine optimization SEO introduced in the embodiment of this application, the technical personnel of this field can understand the specific implementation of the electronic device of this embodiment and its various variations, so how the electronic device implements the method in the embodiment of this application is not introduced in detail here. As long as the technical personnel of this field implement the electronic device used in the keyword analysis and processing method based on search engine optimization SEO in the embodiment of this application, it belongs to the scope of protection of this application.

[0117] Example 4

[0118] This embodiment discloses a computer-readable storage medium, including a memory, a processor, and a computer program stored in the memory and running on the processor. When the processor executes the computer program, it implements any one of the keyword analysis and processing methods based on search engine optimization (SEO) provided by the above methods.

[0119] The above formulas are all dimensionless and numerical calculations. The formula is a formula for the most recent real situation obtained by collecting a large amount of data and performing software simulation. The preset parameters, weights and thresholds in the formula are set by technicians in this field according to actual conditions.

[0120] The above embodiments may be implemented in whole or in part by software, hardware, firmware or any other combination thereof. When implemented by software, the above embodiments may be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, the process or function described in the embodiment of the present invention is generated in whole or in part. The computer may be a general-purpose computer, a special-purpose computer, a computer network or other programmable device. The computer instructions may be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions may be transmitted from one website, computer, server or data center to another website, computer, server or data center via a wired network or a wireless network. The computer-readable storage medium may be any available medium that can be accessed by a computer or a data storage device such as a server or data center that includes one or more available media sets. The available medium may be a magnetic medium (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a DVD) or a semiconductor medium. The semiconductor medium may be a solid-state hard disk.

[0121] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed in the present invention can be implemented in electronic hardware or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present invention.

[0122] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0123] In the several embodiments provided by the present invention, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic, for example, the division of the units is only one, and there may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0124] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0125] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0126] The above is only a specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art can easily think of changes or substitutions within the technical scope disclosed by the present invention, which should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention should be based on the protection scope of the claims.

[0127] Finally: The above description is only a preferred embodiment of the present invention and is not intended 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.

Claims

1. A keyword analysis and processing method based on search engine optimization (SEO), characterized in that: The method comprises: Obtain a keyword library of a target website webpage, wherein the keyword library includes a core keyword and multiple long-tail keywords; Obtain the current exposure of the target website webpage under the initial keyword library, and when the current exposure is lower than the set exposure standard, input the core keyword into the search engine for search, and obtain the completion phrase set automatically provided by the search bar and multiple competing website webpages, wherein the completion phrase set includes Z completion phrases, where Z is an integer greater than zero; Obtain the keyword library of all competing website pages, and perform keyword blank analysis based on the completion phrase set and the keyword library of competing website pages to obtain multiple blank long-tail keywords; The keyword gap analysis based on the completion phrase set and the keyword library of the competing website webpage includes: b1: Get the jth completion phrase in the completion phrase set, and perform word segmentation on the jth completion phrase, remove stop words and core keywords, and obtain the completion word, where j is an integer greater than zero; b2: Extract multiple long-tail keywords from the keyword library of all competing websites, perform word segmentation on all long-tail keywords, remove stop words and core keywords, and obtain multiple feature words; b3: Calculate the similarity between the completion word and each feature word to obtain multiple similarities, sort the multiple similarities from large to small according to the numerical value, compare the first-ranked similarity with the preset similarity threshold, if the first-ranked similarity is greater than or equal to the preset similarity threshold, then mark the jth completion phrase to which the corresponding completion word belongs as a blank long-tail keyword, and set j=j+1, and return to step b1; if the first-ranked similarity is less than the preset similarity threshold, then set j=j+1, and return to step b1; b4: Repeat the above steps b1 to b3 until j=Z, and the loop ends to obtain multiple blank long-tail keywords; Determine the best keyword combination in the keyword library of the target website webpage by using a preconfigured genetic algorithm, expand the keyword library of the target website webpage according to the best keyword combination, and use the expanded keyword library of the target website webpage for search engine optimization; the keyword combination includes the core keyword of the target website webpage, multiple long-tail keywords and at least one blank keyword; The method of using a preconfigured genetic algorithm to determine the best keyword combination in the keyword library of the target website includes: c1: Initialize the population: randomly generate an original population based on multiple blank long-tail keywords, wherein the original population contains E individuals, each of which represents a keyword combination, and E is an integer greater than zero; c2: Fitness evaluation: Under each individual, pre-adjust the keyword library of the target website webpage to obtain the adjusted keyword library of the target website webpage, obtain the exposure of the target website webpage under the adjusted keyword library, input the exposure of the target website webpage under the adjusted keyword library into the pre-built fitness function, and calculate the fitness of each individual; c3: Selection: Use the roulette method to select two individuals with high fitness in the original population as the father and mother; c4: Crossover: Perform crossover operation on the father and mother to produce new individuals; c5: mutation: perform mutation operation on the new individuals to obtain X new individuals, combine the X new individuals into a new population, replace the original population with the new population, and return to step c2; c6: Repeat steps c2 to c5 above until the fitness of individuals in the original population or the new population is greater than or equal to the preset fitness threshold, or the number of iterations is greater than or equal to the preset maximum number of iterations threshold, and output the keyword combination represented by the corresponding individual as the best keyword combination.

2. The keyword analysis and processing method based on search engine optimization (SEO) according to claim 1 is characterized in that: The method for obtaining long-tail keywords in the keyword library of the target website is as follows: a1: Obtain the target website webpage, obtain the text in the target website webpage, perform word segmentation on the text, remove stop words, and perform indexing to obtain a word set containing multiple independent words, each of which is associated with a unique index number; a2: Obtain independent words that are core keywords in the word set, and use independent words that are not core keywords in the word set as candidate words, and obtain the word frequency of each candidate word in the text; a3: Compare the word frequency of each candidate word with the preset first word frequency threshold. If the word frequency is greater than or equal to the preset first word frequency threshold, mark the corresponding candidate word as a candidate word, and obtain Q candidate words; If the word frequency is less than the preset first word frequency threshold, the corresponding candidate word is not marked as an alternative word, and Q is an integer greater than zero; a4: extract the vth candidate word from the Q candidate words, and calculate the minimum character spacing between the vth candidate word and the core keyword according to the index number, where v is an integer greater than zero; a5: Get the set spacing threshold d, determine whether the minimum character spacing of the vth candidate word is less than or equal to the set spacing threshold d, if so, combine the candidate word corresponding to the minimum character spacing with the core keyword to obtain a combined word, and obtain the word frequency of the combined word in the text; if not, set v=v+1, and return to step a4, d is an integer greater than zero, and the value of d is 1; a6: Get the second word frequency threshold, compare the word frequency of the combination word with the second word frequency threshold, if the word frequency of the combination word is greater than or equal to the second word frequency threshold, mark the combination word as a long-tail keyword in the keyword library of the target website webpage, set v=v+1, and return to step a4; if the word frequency of the combination word is less than the second word frequency threshold, set v=v+1, and return to step a4; a7: Repeat the above steps a4 to a6 until v=Q and the loop ends, obtaining multiple long-tail keywords belonging to the keyword library of the target website.

3. The keyword analysis and processing method based on search engine optimization (SEO) according to claim 2 is characterized in that: The step of obtaining the current exposure of the target website webpage under the initial keyword library includes: Obtain exposure data of core keywords in the keyword library of the target website's webpage, as well as exposure data of each long-tail keyword; the exposure data includes search volume, ranking in search results, and click-through rate; The exposure data of core keywords and long-tail keywords are input into the pre-built exposure calculation model to obtain the current exposure of the target website web page under the initial keyword library.

4. The keyword analysis and processing method based on search engine optimization (SEO) according to claim 3 is characterized in that: The expression of the pre-built exposure calculation model is as follows: ; Where: is the current exposure, The search volume of the core keywords. The ranking of core keywords in search results. The click-through rate of the core keywords. is the search volume of the i-th long-tail keyword, is the ranking of the i-th long-tail keyword in the search results, is the click-through rate of the i-th long-tail keyword.

5. The keyword analysis and processing method based on search engine optimization (SEO) according to claim 4 is characterized in that: The calculation formula of the pre-constructed fitness function is: ; Where: For fitness, is a natural constant, To increase the exposure of the target website pages under the adjusted keyword library.

6. A keyword analysis and processing system based on search engine optimization (SEO), characterized in that: The system is implemented based on the keyword analysis and processing method based on search engine optimization (SEO) according to any one of claims 1 to 5, and the system comprises: A data acquisition module is used to acquire a keyword library of a target website webpage, wherein the keyword library includes a core keyword and multiple long-tail keywords; An exposure judgment module is used to obtain the current exposure of the target website webpage under the initial keyword library, and when the current exposure is lower than the set exposure standard, input the core keyword into the search engine for search, and obtain the completion phrase set automatically provided by the search bar and multiple competing website webpages, wherein the completion phrase set includes Z completion phrases, where Z is an integer greater than zero; A blank analysis module is used to obtain the keyword library of all competing website pages, and perform keyword blank analysis based on the completion phrase set and the keyword library of the competing website pages to obtain multiple blank long-tail keywords; The SEO optimization module is used to determine the best keyword combination in the keyword library of the target website web page using a preconfigured genetic algorithm, expand the keyword library of the target website web page according to the best keyword combination, and use the expanded keyword library of the target website web page to perform search engine optimization.

7. An electronic device comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that: When the processor executes the computer program, the keyword analysis and processing method based on search engine optimization (SEO) described in claims 1-5 is implemented.

8. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and when the computer program is executed, the keyword analysis and processing method based on search engine optimization (SEO) described in claims 1-5 is implemented.

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