Stock screening star-beating scoring method and system based on quantitative model

Abstract

The invention provides a stock screening star-beating scoring method and system based on a quantitative model, and belongs to the technical field of data processing, and the method specifically comprises the steps: determining a potential retrieval target in a stock based on a retrieval keyword of a user and a retrieval keyword associated with retrieval times, obtaining similar conditions of retrieval keywords of historical users in different potential retrieval targets, the retrieval keywords of the users and the retrieval keywords associated with the retrieval times, determining historical reference users of the users according to the similar conditions, and determining the retrieval times of the users according to the matching conditions of different stocks and the retrieval keywords of the historical reference users. And the grading star-beating results of different stocks and the matching retrieval target of the user are determined by using the quantitative model, so that the matching accuracy of the retrieval result is improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of data processing, and in particular relates to a stock screening and star rating method and system based on a quantitative model. Background Art

[0002] In order to implement the stock screening process, the invention patent application CN202310750041.1 "A Stock Screening Method and System" obtains the stock information to be retrieved, converts the stock information into a digital vector, obtains the first semantic vector, and uses the matching situation of the first semantic vector and the stock information to determine the total score, so that the user can select the target stock according to the total score. However, there are the following technical defects: In the process of screening stocks based on the user's search results, the existing technical solutions ignore the adjustment of the user's search keywords. Since the specific user is not familiar with the keywords of the search target, he may determine the stock through multiple searches within a certain period of time. As a result, the search target matching the user's search keyword may have been pushed and determined to be mismatched. If the search keyword cannot be identified and adjusted, the matching accuracy of the search results cannot be guaranteed.

[0003] In response to the above technical problems, the present application specifically provides a stock screening and star scoring method and system based on a quantitative model. Summary of the invention

[0004] To achieve the purpose of the present invention, the present invention adopts the following technical solutions: Specifically, in the first aspect, the present application provides a stock screening and star scoring method based on a quantitative model, which specifically includes: S1 determines the search keyword of the user in the current search data, takes the search times of the user in the preset time as the reference search times, and when it is determined that there is a related search times in the reference search times according to the matching of the search keywords with the reference search times in different stocks, proceeds to the next step; S2 determines a matching search target of the user's search keyword, and when it is determined that there is a deviation in the user's search keyword based on the push data of the matching search target in the associated search times, determines a potential search target in the stock based on the user's search keyword and the search keyword of the associated search times; S3 obtains the search keywords of historical users at different potential search targets, and the similarities between the search keywords of the user and the search keywords with associated search times, and determines the historical reference users of the user by using the similarities; S4 determines the rating and star results of different stocks and the matching search target of the user by using a quantitative model according to the matching conditions between different stocks and the search keywords of the historical reference user.

[0005] The beneficial effects of the present invention are: By matching the push data of the search target in the associated search times, it is determined whether there is a deviation in the user's search keywords, thereby realizing the identification of the deviation of the search keywords from the push situation in the associated search times, avoiding the technical problem of low accuracy of the search results caused by the single use of the user's search keywords when the user's search keywords have deviations. At the same time, it also realizes differentiated search push processing for users based on the deviation identification processing results, ensuring the accuracy of push processing.

[0006] According to the matching of different stocks with the search keywords of historical reference users, the quantitative model is used to determine the rating and star results of different stocks, avoiding the technical problem of low accuracy of retrieval processing results caused by traditional reliance on user search keywords. It realizes the determination of stock rating and star results from the search keywords of historical reference users, ensuring the reliability and accuracy of push processing of matching retrieval targets.

[0007] A further technical solution is that the search keyword is determined based on the analysis result of the user's search data.

[0008] A further technical solution is that the matching status of the search keywords with reference search times in different stocks is determined according to the stocks for which both the search keywords of the current search data and the search keywords with reference search times exist.

[0009] A further technical solution is that the method for determining the number of associated searches is: According to the matching situation of the search keywords of the reference search times in different stocks, determining the coexistence of different search keywords of the current search data and different search keywords in the reference search times in different stocks; Determine the number of stocks that exist simultaneously with different search keywords currently being searched by using the simultaneous existence situation, and use the number as the number of associated stocks; Based on the sum of the numbers of associated stocks of different search keywords of the current search data, the total of associated stocks is determined, and according to the total of associated stocks, it is determined whether the reference search number is the associated search number of the current search data.

[0010] A further technical solution is that when the sum of the associated stocks is greater than a preset associated stock quantity threshold, the reference search number is determined to be the associated search number of the current search data.

[0011] A further technical solution is that, when the user does not have any associated search times, the same number of search keywords of the current search data and the stock keywords are used to determine the rating and star results of different stocks based on the proportion of the number of keywords of the stock, and the stocks with rating and star results greater than the preset rating are used as the user's matching search target.

[0012] A further technical solution is to use a quantitative model to determine the rating and star result of the stock, specifically including: Using a quantitative model to determine the average value of stock keyword matching coefficients of different historical reference users; The average value of the stock keyword matching coefficients of different historical reference users is used to determine the rating result of the stock.

[0013] A further technical solution is to determine the rating result of the stock based on the average value of the stock keyword matching coefficients of different historical reference users, specifically including: The average value of the stock keyword matching coefficients with different historical reference users is used as the scoring result; The star rating result of the stock is determined based on the preset star rating corresponding to the rating result.

[0014] A further technical solution is that the star rating results include one star, two stars and three stars.

[0015] A further technical solution is that the user's matching search target is stocks whose star rating results are greater than a preset rating.

[0016] In a second aspect, the present invention provides a computer system comprising: a memory and a processor that are communicatively connected, and a computer program stored in the memory and capable of running on the processor, wherein the processor executes the above-mentioned quantitative model-based stock screening and star scoring method when running the computer program.

[0017] Other features and advantages will be described in the following description. The objects and other advantages of the present invention are realized and obtained by the structures particularly pointed out in the description and drawings.

[0018] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, preferred embodiments are given below and described in detail with reference to the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] The above and other features and advantages of the present invention will become more apparent by describing in detail exemplary embodiments thereof with reference to the accompanying drawings; Figure 1 It is a flow chart of a stock screening and star scoring method based on a quantitative model; Figure 2 is a flow chart of a method for determining the number of associated searches; Figure 3 is a flow chart of a method for determining potential retrieval targets in stocks; Figure 4 is a flow chart of a method for determining a user's historical reference user; Figure 5 It is a flowchart of the method for determining the star rating results of stocks. DETAILED DESCRIPTION

[0020] In order to enable those skilled in the art to better understand the technical solutions in this specification, the technical solutions in the embodiments of this specification will be clearly and completely described below in conjunction with the drawings in the embodiments of this specification. Obviously, the described embodiments are only part of the embodiments of this specification, not all of the embodiments. Based on the embodiments of this specification, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of this specification.

[0021] In the present application, the push status of the user's search target in the associated search times is utilized to realize the situation where the search target of the user's search keyword has been pushed in different associated search times but has not been matched. Then, the user's search keyword that is associated with the user's search keyword can be used to match the search target and determine the star rating results, thereby ensuring the accuracy of the search processing results.

[0022] Example 1 like Figure 1 As shown, the present application provides a stock screening and star scoring method based on a quantitative model, which specifically includes: S1 determines the search keyword of the user in the current search data, takes the search times of the user in the preset time as the reference search times, and when it is determined that there is a related search times in the reference search times according to the matching of the search keywords with the reference search times in different stocks, proceeds to the next step; It should be noted that, based on the different search keywords of the current search data and the different search keywords in the reference search times that exist simultaneously in different stocks, the number of stocks in which the different search keywords of the current search exist simultaneously is determined, and is used as the number of associated stocks. The search keywords whose number of associated stocks is greater than the preset associated number threshold are used as associated keywords. When the proportion of the associated keywords of the current search data in the search keywords is greater than 0.6, the reference search times are determined to be the associated search times of the current search data.

[0023] S2 determines a matching search target of the user's search keyword, and when it is determined that there is a deviation in the user's search keyword based on the push data of the matching search target in the associated search times, determines a potential search target in the stock based on the user's search keyword and the search keyword of the associated search times; Specifically, when the matching search targets have been pushed in the associated search times, it is determined that there is a deviation in the user's search keywords, and reference keywords are constructed based on the search keywords of the user's current search data and the search keywords of the associated search times. The potential search targets in stocks are stocks whose matching number of reference keywords is greater than the preset reference keyword number.

[0024] S3 obtains the search keywords of historical users at different potential search targets, and the similarities between the search keywords of the user and the search keywords with associated search times, and determines the historical reference users of the user by using the similarities; The historical reference users are historical users whose search keywords and reference keywords of different potential search targets have the same number, and whose number of reference keywords accounts for more than 0.5.

[0025] S4 determines the rating and star results of different stocks and the matching search target of the user by using a quantitative model according to the matching conditions between different stocks and the search keywords of the historical reference user.

[0026] The stock keyword matching coefficient with different historical reference users is determined based on the number of matches between the stock and the historical reference users' search keywords in potential search targets, and the proportion of the number of search keywords of the historical reference users in potential search targets. The stock's rating result is determined based on the average value of the stock keyword matching coefficients with different historical reference users.

[0027] Furthermore, the search keyword is determined based on the parsing result of the user's search data.

[0028] Specifically, the matching status of the search keyword with the reference search times in different stocks is determined according to the stocks in which both the search keyword of the current search data and the search keyword with the reference search times exist.

[0029] Specifically, Figure 2 As shown, the method for determining the number of associated searches is: According to the matching situation of the search keywords of the reference search times in different stocks, determining the coexistence of different search keywords of the current search data and different search keywords in the reference search times in different stocks; Determine the number of stocks that exist simultaneously with different search keywords currently being searched by using the simultaneous existence situation, and use the number as the number of associated stocks; Based on the sum of the numbers of associated stocks of different search keywords of the current search data, the total of associated stocks is determined, and according to the total of associated stocks, it is determined whether the reference search number is the associated search number of the current search data.

[0030] Further, when the sum of the associated stocks is greater than a preset associated stock quantity threshold, the reference search number is determined to be the associated search number of the current search data.

[0031] It should also be noted that when the user does not have any associated search times, the same number of search keywords of the current search data and the stock keywords are used to determine the rating and star results of different stocks based on the proportion of the number of keywords of the stock, and the stocks with rating and star results greater than the preset rating are used as the user's matching search target.

[0032] In another possible embodiment, the method for determining the number of associated retrieval times is: According to the matching situation of the search keywords of the reference search times in different stocks, determining the coexistence of different search keywords of the current search data and different search keywords in the reference search times in different stocks; Determine the number of stocks that exist simultaneously with different search keywords currently being searched by using the co-existence situation, and use it as the number of associated stocks, and use the search keywords whose number of associated stocks is greater than a preset associated number threshold as associated keywords; Based on the proportion of the associated keywords of the current search data in the search keywords, it is determined whether the reference search times is the associated search times of the current search data.

[0033] Further, when the proportion of the associated keywords of the current search data in the search keywords is greater than the preset associated keyword proportion, the reference search number is determined to be the associated search number of the current search data.

[0034] In another possible embodiment, the method for determining the number of associated retrieval times is: Using the same number of search keywords of the current search data and the keywords of the stock, the stocks whose keyword quantity ratio of the stocks is greater than the preset keyword quantity ratio are used as matching search targets, and when the same number of matching search targets and matching search targets of the reference search times is greater than the preset search target quantity, the reference search times are determined to be the associated search times of the current search data; When the number of the matching search targets that are identical to the matching search targets of the reference search times is not greater than the preset number of search targets: Obtaining a proportion of the same number in the matching search targets of the current search data, and when the proportion of the same number in the matching search targets of the current search data is greater than a preset proportion of the same number, determining the reference search number as the associated search number of the current search data; When the proportion of the same number in the matching search targets of the current search data is not greater than the preset proportion of the same number: According to the matching situation of the search keywords of the reference search times in different stocks, determining the coexistence of different search keywords of the current search data and different search keywords in the reference search times in different stocks; The number of stocks that exist simultaneously with different search keywords of the current search is determined by using the simultaneous existence situation, and the number is used as the number of associated stocks. When the sum of the numbers of associated stocks of different search keywords of the current search data is greater than a preset threshold of the number of associated stocks, the reference search number is determined to be the number of associated searches of the current search data; When the sum of the number of stocks associated with different search keywords in the current search data is not greater than the preset threshold of the number of associated stocks: When there is no search keyword with a number of associated stocks greater than a preset associated number threshold, it is determined that the reference search number does not belong to the associated search number of the current search data; The search keyword with the number of associated stocks greater than the preset associated number threshold is used as the associated keyword, and when the proportion of the associated keywords of the current search data in the search keywords is greater than the preset associated keyword proportion, the reference search number is determined to be the associated search number of the current search data; When the proportion of the associated keywords of the current search data in the search keywords is greater than the proportion of the preset associated keywords: The correlation coefficient between the current search data and the reference search times is determined based on the number of stocks associated with different associated keywords of the current search data, and the correlation coefficient is used to determine whether the reference search times are the associated search times of the current search data.

[0035] Further, when the correlation coefficient is greater than a preset correlation coefficient threshold, the reference search number is determined to be the correlation search number of the current search data.

[0036] Specifically, the matching search target of the user's search keyword is the same number of search keywords of the current search data and the stock keywords, and the number of the stock keywords accounts for a greater proportion than the preset keyword number.

[0037] Specifically, determining that there is a deviation in the search keyword of the user specifically includes: When the matching search targets have all been pushed in the associated search times, it is determined that there is a deviation in the user's search keyword.

[0038] It should be noted that if Figure 3 As shown, the method for determining the potential search target in the stock is: Constructing reference keywords based on the search keywords of the current search data of the user and the search keywords associated with the search times; Based on the matching numbers of different stocks and reference keywords, potential search targets in the stocks are determined.

[0039] Furthermore, the potential search targets in the stocks are stocks whose matching number of reference keywords is greater than the preset number of reference keywords.

[0040] Optionally, the method for determining the potential search target in the stock is: Constructing reference keywords based on the search keywords of the current search data of the user and the search keywords associated with the search times; Determining matching reference keywords for the stocks based on matching conditions between different stocks and reference keywords; According to the matching condition between the matching reference keyword and the current search data and the associated search times, it is determined whether the stock is a potential search target.

[0041] It should be noted that when the matching search keyword is in the current search data and the number of associated searches for the matching search keyword is greater than a preset threshold of associated searches, the stock is determined to be a potential search target.

[0042] Optionally, the method for determining the potential search target in the stock is: Constructing reference keywords based on the search keywords of the current search data of the user and the search keywords associated with the search times; Based on the matching conditions between different stocks and reference keywords, when the number of reference keywords matched by the stock does not meet the requirement, it is determined that the stock does not belong to the potential search target; When the number of reference keywords matched by the stock meets the requirement: Using the reference keywords matched by the stock as matching reference keywords, and when the number of the matching reference keywords is greater than the preset number of matching reference keywords, determining that the stock belongs to a potential search target; When the number of the matching reference keywords is not greater than the preset number of matching reference keywords: When the matching reference keyword is not in the current search data, it is determined that the stock does not belong to the potential search target; When the matching reference search term is in the current search data: Obtaining a ratio of the number of matching reference keywords of the stock in the search keywords of the current search data, and when the ratio of the number of matching reference keywords of the stock in the search keywords of the current search data is greater than a preset ratio threshold, determining that the stock is a potential search target; When the proportion of the matching reference keywords of the stock in the search keywords of the current search data is not greater than the preset proportion threshold: When the number of associated searches matching the search keyword is greater than a preset associated search number threshold, the stock is determined to be a potential search target; When the number of associated searches matching the search keyword is not greater than the preset associated search number threshold: The stock matching coefficient of the stock is determined based on the proportion of the matching reference keywords in the search keywords of the current search data and the proportion of the matching reference keywords in the search keywords of different associated search times, and the stock matching coefficient of the stock is used to determine whether the stock is a potential search target.

[0043] Further, when the stock matching coefficient of the stock is greater than a preset stock matching coefficient threshold, the stock is determined to be a potential retrieval target.

[0044] Specifically, Figure 4 As shown, the method for determining the historical reference user of the user is: Constructing reference keywords based on the search keywords of the current search data of the user and the search keywords associated with the search times; Based on the search keywords of historical users at different potential search targets, determining the number of matches between the search keywords of the historical users and the reference keywords; By using the matching number between the search keyword of the historical user and the reference keyword, it is determined whether the historical user is a historical reference user of the user.

[0045] Further, when the number of matches between the search keyword of the historical user and the reference keyword is greater than a preset matching number threshold, the historical user is determined to be a historical reference user of the user.

[0046] Optionally, the method for determining the historical reference user of the user is: S31 constructs reference keywords based on the search keywords of the current search data of the user and the search keywords of the associated search times, determines the number of matches between the search keywords of the historical users and the reference keywords based on the search keywords of the historical users at different potential search targets, and determines the basic matching coefficient using the proportion of the number of matches in the reference keywords and the distribution of the number of matches at different associated search times and the current search data; S32 determines the target matching coefficient of the historical user at different potential search targets based on the number of matches between the historical user's search keyword and the reference keyword at different potential search targets, and determines the comprehensive target matching coefficient of the historical user based on the target matching coefficient at different potential search targets; S33 determines the user matching coefficient between the historical user and the user based on the average value of the comprehensive target matching coefficient and the basic matching coefficient of the historical user, and determines whether the historical user is a historical reference user of the user by using the user matching coefficient.

[0047] Further, when the user matching coefficient is greater than a preset user matching coefficient threshold, the historical user is determined to be a historical reference user of the user.

[0048] Optionally, the above step S31 includes the following contents: S311: Based on the search keywords of the current search data of the user and the search keywords of the associated search times, reference keywords are constructed; based on the search keywords of historical users with different potential search targets, the number of matches between the search keywords of the historical user and the reference keywords is determined; when the number of matches between the search keywords of the historical user and the reference keywords does not meet the requirement, it is determined that the historical user does not belong to the historical reference user of the user; when the number of matches between the search keywords of the historical user and the reference keywords meets the requirement, the process proceeds to step S312; S312: when the number of matches between the historical user's search keyword and the reference keyword is greater than the preset search keyword match number threshold, it is determined that the historical user belongs to the user's historical reference user; when the number of matches between the historical user's search keyword and the reference keyword is not greater than the preset search keyword match number threshold, the process proceeds to step S313; S313 determines a basic matching coefficient by using the proportion of the number of matches in the reference keyword and the distribution of different associated search times and current search data. When the basic matching coefficient is greater than a preset basic matching coefficient threshold, it is determined that the historical user belongs to the historical reference user of the user. When the basic matching coefficient is not greater than the preset basic matching coefficient threshold, the process proceeds to step S314. S314: When the basic matching coefficient is less than the preset matching coefficient value, it is determined that the historical user belongs to the historical reference user of the user; when the basic matching coefficient is not less than the preset matching coefficient value, the process proceeds to step S32.

[0049] Optionally, the above step S32 includes the following contents: S321 determines the target matching coefficient of the historical user at different potential search targets based on the number of matches between the search keyword of the historical user at different potential search targets and the reference keyword. If there is no potential search target whose matching target coefficient meets the requirement, it is determined that the historical user does not belong to the reference historical user of the user. If there is a potential search target whose matching target coefficient meets the requirement, the process proceeds to step S322. S322: When the number of potential search targets that meet the requirements of the matching target coefficient is greater than the preset number of potential search targets, it is determined that the historical user belongs to the reference historical user of the user; when the number of potential search targets that meet the requirements of the matching target coefficient is not greater than the preset number of potential search targets, the process proceeds to step S323; S323 determines the comprehensive target matching coefficient of the historical user according to the target matching coefficients of different potential retrieval targets. When the comprehensive target matching coefficient of the historical user meets the requirement, it is determined that the historical user belongs to the reference historical user of the user. When the comprehensive target matching coefficient of the historical user does not meet the requirement, it goes to step S33.

[0050] Specifically, Figure 5 As shown, the method for determining the rating result of the stock is: According to the matching conditions between different stocks and the search keywords of the historical reference user, determining the matching quantity between the stock and the search keywords of the historical reference user in the potential search target; Determine the stock keyword matching coefficients of stocks and different historical reference users according to the number of matches with the search keywords of the historical reference user in the potential search target and the proportion of the number of search keywords of the historical reference user in the potential search target; The stock keyword matching coefficients with different historical reference users are used to determine the rating and star results of the stock using a quantitative model.

[0051] Furthermore, the quantitative model is used to determine the rating and star result of the stock, specifically including: Using a quantitative model to determine the average value of stock keyword matching coefficients of different historical reference users; The average value of the stock keyword matching coefficients of different historical reference users is used to determine the rating result of the stock.

[0052] It should be noted that the stock rating result is determined based on the average value of the stock keyword matching coefficients of different historical reference users, specifically including: The average value of the stock keyword matching coefficients with different historical reference users is used as the scoring result; The star rating result of the stock is determined based on the preset star rating corresponding to the rating result.

[0053] Furthermore, the star rating results include one star, two stars and three stars.

[0054] Optionally, the user's matching search target is a stock whose star rating result is greater than a preset rating.

[0055] Example 2 In a second aspect, the present invention provides a computer system comprising: a memory and a processor that are communicatively connected, and a computer program stored in the memory and capable of running on the processor, wherein the processor executes the above-mentioned quantitative model-based stock screening and star scoring method when running the computer program.

[0056] Each embodiment in this specification is described in a progressive manner, and the same or similar parts between the embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the device, equipment, and non-volatile computer storage medium embodiments, since they are basically similar to the method embodiments, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment.

[0057] The above is a description of a specific embodiment of the specification. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recorded in the claims can be performed in an order different from that in the embodiments and still achieve the desired results. In addition, the processes depicted in the drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0058] The above description is only one or more embodiments of this specification and is not intended to limit this specification. For those skilled in the art, one or more embodiments of this specification may have various changes and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of one or more embodiments of this specification shall be included in the scope of the claims of this specification.

Claims

1. A stock screening and star rating method based on a quantitative model, characterized in that: Specifically include: Determine the search keyword of the user in the current search data, take the search times of the user in the preset time as the reference search times, and when it is determined that there is a related search times in the reference search times according to the matching of the search keywords with the reference search times in different stocks, proceed to the next step; Determine a matching search target of the user's search keyword, and when it is determined that there is a deviation in the user's search keyword based on the push data of the matching search target in the associated search times, determine a potential search target in the stock based on the user's search keyword and the search keyword of the associated search times; Obtaining search keywords of historical users at different potential search targets, and similarities between the search keywords of the user and the search keywords with associated search times, and determining historical reference users of the user by using the similarities; According to the matching conditions between different stocks and the search keywords of the historical reference user, the quantitative model is used to determine the rating and star results of different stocks and the matching search target of the user.

2. The method for stock screening and scoring based on a quantitative model as claimed in claim 1, characterized in that: The search keyword is determined based on the analysis result of the user's search data.

3. The method for stock screening and scoring based on a quantitative model as claimed in claim 1, characterized in that: The matching status of the search keyword with the reference search times in different stocks is determined according to the stocks in which both the search keyword of the current search data and the search keyword with the reference search times exist.

4. The method for stock screening and scoring based on a quantitative model as claimed in claim 1, characterized in that: The method for determining the number of associated searches is: According to the matching situation of the search keywords of the reference search times in different stocks, determining the coexistence of different search keywords of the current search data and different search keywords in the reference search times in different stocks; Determine the number of stocks that exist simultaneously with different search keywords currently being searched by using the simultaneous existence situation, and use the number as the number of associated stocks; Based on the sum of the numbers of associated stocks of different search keywords of the current search data, the total of associated stocks is determined, and according to the total of associated stocks, it is determined whether the reference search number is the associated search number of the current search data.

5. The method for stock screening and scoring based on a quantitative model as claimed in claim 4, characterized in that: When the sum of the associated stocks is greater than a preset associated stock quantity threshold, the reference search number is determined to be the associated search number of the current search data.

6. The method for stock screening and scoring based on a quantitative model as claimed in claim 1, characterized in that: When the user does not have any associated search times, the same number of search keywords of the current search data and the keywords of the stock are used to determine the rating and star results of different stocks based on the proportion of the number of keywords of the stock, and the stocks with rating and star results greater than the preset rating are used as the user's matching search target.

7. The method for stock screening and scoring based on a quantitative model as claimed in claim 1, characterized in that: The method for determining the rating star result of the stock is: According to the matching conditions between different stocks and the search keywords of the historical reference user, determining the matching quantity between the stock and the search keywords of the historical reference user in the potential search target; Determine the stock keyword matching coefficients of stocks and different historical reference users according to the number of matches with the search keywords of the historical reference user in the potential search target and the proportion of the number of search keywords of the historical reference user in the potential search target; The stock keyword matching coefficients with different historical reference users are used to determine the rating and star results of the stock using a quantitative model.

8. The method for stock screening and scoring based on a quantitative model as claimed in claim 7, characterized in that: The quantitative model is used to determine the rating and star results of the stocks, including: Using a quantitative model to determine the average value of stock keyword matching coefficients of different historical reference users; The average value of the stock keyword matching coefficients of different historical reference users is used to determine the rating result of the stock.

9. The method for stock screening and scoring based on a quantitative model as claimed in claim 8, characterized in that: The stock rating result is determined based on the average value of the stock keyword matching coefficients of different historical reference users, specifically including: The average value of the stock keyword matching coefficients with different historical reference users is used as the scoring result; The star rating result of the stock is determined based on the preset star rating corresponding to the rating result.

10. A computer system comprising: A memory and a processor that are communicatively connected, and a computer program stored in the memory and capable of running on the processor, characterized in that when the processor runs the computer program, a quantitative model-based stock screening and star scoring method as described in any one of claims 1 to 9 is executed.

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