Network search method, electronic equipment and storage medium

Abstract

The invention provides a network search method, electronic equipment and a storage medium. The method comprises the steps that search result sets obtained through multiple network search modes are obtained, and each search combination set comprises multiple result documents obtained through searching in the corresponding network search mode and the sorting sequence number of each result document in the corresponding network search mode; according to the weights corresponding to the various network search modes and the sorting sequence numbers of the result documents in the search combination sets, comprehensively sorting the result documents in the search combination sets; and feeding back each result document after comprehensive sorting. The search result sets of various network search modes are obtained, and the result documents in the search result sets are comprehensively sorted, so that each fed-back sorted result document comprises the search results of various network search modes; and the search results of the network search modes are integrated through the comprehensive sorting, so that the problems in the prior art are solved.

Description

Technical Field

[0001] The present application relates to the field of Internet technology, and in particular to a network search method, an electronic device and a storage medium. Background Art

[0002] With the development of science and technology, people often need to search the Internet to obtain relevant Internet information in their daily lives, such as obtaining product information, news information, driving routes, etc. through Internet searches.

[0003] There are many ways to search the Internet. For example, users can search through search engines such as Baidu, ES (Elasticsearch), Google, etc., or through large models such as GPT (Generative Pre-Trained). These different search methods can all provide feedback on search results based on the search terms entered by the user. Since different Internet search methods have their own search strategies, these different search strategies all have their shortcomings. For example, a single search strategy may easily miss results or the ranking of search results may not meet user needs. Therefore, considering the importance of Internet search in users' daily lives, the problems existing in this single search strategy for Internet search methods need to be solved. Summary of the invention

[0004] The purpose of the embodiments of the present application is to provide a network search method, an electronic device and a storage medium, which are used to solve the problem of low efficiency of table generation in the prior art.

[0005] A first aspect of an embodiment of the present application provides a network search method, the method comprising:

[0006] Obtaining search result sets obtained by multiple network search methods, wherein each search combination set includes multiple result documents obtained by searching the corresponding network search method, and the sorting sequence number of each result document in the corresponding network search method;

[0007] According to the weights corresponding to various network search methods and the sorting numbers of the result documents in each search combination set, the result documents in each search combination set are comprehensively sorted;

[0008] Feedback each result document after comprehensive sorting.

[0009] Preferably, the result documents in each search combination set are comprehensively sorted according to the weights corresponding to the various network search methods and the sorting numbers of the result documents in each search combination set, specifically including:

[0010] For each result document in each search combination set, determine the importance score of the result document according to the ranking sequence number of the result document in the corresponding network search method;

[0011] For each result document in each search combination set, determine a comprehensive score of the result document according to the importance score of the result document and the weight of the network search method corresponding to the result document;

[0012] According to the comprehensive scores of the result documents in each search combination, each result document is comprehensively ranked.

[0013] Preferably, for each result document in each search combination set, a comprehensive score of the result document is determined according to the importance score of the result document and the weight of the network search method corresponding to the result document, specifically including:

[0014] For each result document in each search combination set, the importance score of the result document is multiplied by the weight of the network search method corresponding to the result document to obtain a comprehensive score of the result document.

[0015] Preferably, the method further comprises:

[0016] For each result document in each search combination set, determining whether the result document exists in at least two search combination sets;

[0017] In the case that the result document exists in at least two search combination sets, the maximum comprehensive score of the result document is obtained, and the maximum comprehensive score is multiplied by the magnification factor to serve as the comprehensive score of the result document.

[0018] Preferably, the method further comprises:

[0019] The weights corresponding to various network search methods are determined according to the user activity and the proportion of positive feedback corresponding to each network search method.

[0020] Preferably, obtaining search result sets obtained by multiple network search methods respectively includes:

[0021] Get the search term entered by the user;

[0022] The search term is input into the input interface of multiple network search methods to obtain the search result sets respectively fed back by the multiple network search methods.

[0023] A second aspect of an embodiment of the present application provides an electronic device, including:

[0024] Memory for storing computer programs;

[0025] A processor is used to execute any one of the methods described in the method embodiments of the present application.

[0026] A third aspect of an embodiment of the present application provides a storage medium, including: a program, which, when executed on an electronic device, enables the electronic device to execute any one of the methods in the method embodiments of the present application.

[0027] The method provided by the embodiment of the present application includes obtaining the search result sets obtained by various network search methods, and then comprehensively sorting the result documents in each search combination set according to the weights corresponding to the various network search methods and the sorting sequence numbers of the result documents in each search combination set, and then feeding back the comprehensively sorted result documents. In this method, since the search result sets of multiple network search methods are obtained, and the result documents in these search result sets are comprehensively sorted according to the weights corresponding to the various network search methods and the sorting sequence numbers of the result documents in each search combination set, the sorted result documents fed back include the search results of various network search methods, and the search results of these network search methods are integrated through the comprehensive sorting. Therefore, compared with the current single network search method, the search results are more accurate and effective, and the problems in the prior art are solved. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings required for use in the embodiments of the present application will be briefly introduced below. It should be understood that the following drawings only show certain embodiments of the present application and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without paying creative work.

[0029] Figure 1 A schematic diagram of the structure of an electronic device provided in one embodiment of the present application.

[0030] Figure 2 A schematic diagram of a specific flow chart of a network search method provided in an embodiment of the present application;

[0031] Figure 3 A schematic diagram of the specific structure of a network search device provided in another embodiment of the present application. DETAILED DESCRIPTION

[0032] The technical solutions in the embodiments of the present application will be described below in conjunction with the accompanying drawings in the embodiments of the present application. In the description of the present application, terms such as "first", "second", "third", etc. are only used to distinguish the description and cannot be understood as indicating or implying relative importance or sequence.

[0033] As mentioned above, different online search methods have their own search strategies, and these different search strategies all have their shortcomings. For example, a single search strategy may easily miss detections or the sorting of search results may not meet user needs. Therefore, considering the importance of online search in users' daily lives, the problems existing in this single search strategy for online search methods need to be solved.

[0034] For example, the ES search engine usually matches the search terms entered by the user with the words in the document to obtain the result document; large models such as GPT usually interact with users through search terms to identify the user's intentions, and then obtain the result document based on the user's intentions; in addition, there is a network search method that converts the search terms entered by the user into vectors, and then performs similarity matching with the document's vectors, and obtains the result document based on the similarity matching results. The search strategies adopted by these different network search methods all have their shortcomings, such as easy to miss detection or the ranking of search results does not meet user needs, which ultimately leads to reduced accuracy of search results. Therefore, it is necessary to provide a more accurate network search method.

[0035] In view of this, the embodiments of the present application provide a network search method, device, electronic device and storage medium, which can be used to solve the technical problem. Figure 1 FIG. 1 is a schematic diagram of a specific structure of an electronic device provided in this embodiment. The electronic device 1 includes: at least one processor 11 and a memory 12. Figure 1 A processor is used as an example. The processor 11 and the memory 12 may be connected via a bus 10, and the memory 12 stores instructions that can be executed by the processor 11, and the instructions are executed by the processor 11, so that the electronic device 1 can execute all or part of the process of the method in the embodiment of the present application.

[0036] The electronic device 1 may be a client electronic device, such as a user's mobile phone, computer, etc. The electronic device 1 may also be a server electronic device, such as a server node, etc.

[0037] In one embodiment, the electronic device 1 serves as a client electronic device (hereinafter referred to as the client), so that the user can input search terms into the client, thereby enabling the client to execute the method provided in the embodiment of the present application.

[0038] In one embodiment, the electronic device 1 serves as a server-side electronic device (hereinafter referred to as a server). At this time, the server can connect to multiple clients, thereby being able to obtain the search terms sent by the user through the client and execute the method provided in the embodiment of the present application.

[0039] like Figure 2The figure is a schematic diagram of a specific flow chart of the network search method provided by the embodiment of the present application. The method can be performed by Figure 1 The method is performed by the electronic device 1 shown in the figure. Here, the method is described by taking the electronic device 1 as a server to perform the method. In this case, the method includes the following steps:

[0040] Step S21: Obtain search result sets obtained by various network search methods.

[0041] Each search combination set includes a plurality of result documents obtained by searching in a corresponding network search method, and a sorting sequence number of each result document in the corresponding network search method.

[0042] The multiple network search methods may include, for example, ES search engine, Baidu search, Google search, GPT large model search, etc. In actual applications, it is usually possible to first obtain the search term entered by the user. Specifically, the user can enter the search term through the client, so that the server can obtain the search term entered by the user through the client, and then input the search term into the input interface of the multiple network search methods, thereby obtaining the search result sets respectively fed back by the multiple network search methods.

[0043] For example, the search term can be input into the input interface of the ES search engine, so that the ES search engine can search for a search result set based on the search term and feedback the search result set to the server; of course, the search term can also be input into the input interface of Baidu search, the input interface of the GPT large model, etc., and then the search result sets fed back by Baidu search and the GPT large model can be obtained.

[0044] It should be noted that various current network search methods can obtain various result documents based on search terms on the one hand, and can also sort these result documents on the other hand, thereby obtaining the sorting sequence number of the result document in the network search method. For example, taking Baidu search as an example, the search engine of Baidu search can obtain various result documents based on the search terms entered by the user, and sort these result documents, thereby obtaining the sorting sequence number of each result document in Baidu search, and the sorting sequence number reflects the importance ranking of the result document in the search result set in the search strategy of the corresponding network search method. Therefore, in the embodiment of the present application, each search combination set includes multiple result documents searched by the corresponding network search method, and the sorting sequence number of each result document in the corresponding network search method.

[0045] For example, the search combination set fed back by Baidu Search for a search term includes multiple result documents and the sorting numbers of these result documents. The sorting numbers of these result documents reflect the importance ranking of the result document in the search result set in the search strategy of Baidu Search.

[0046] Step S22: comprehensively sorting the result documents in each search combination set according to the weights corresponding to the various network search methods and the sorting sequence numbers of the result documents in each search combination set.

[0047] After obtaining the search result sets of multiple network search methods through the above step S21, the function of step S22 is to comprehensively sort the result documents in each search combination set, so as to achieve the integration of these result documents. Among them, since each network search method uses its own search strategy to search and then obtains the corresponding search result set, and the result documents in the search result set also have the sorting sequence number in the corresponding network search method, it is usually difficult to directly integrate the result documents in these different search result sets.

[0048] In the embodiment of the present application, corresponding weights are set for various network search methods, and then the result documents in each search combination set are comprehensively sorted according to the weights corresponding to the various network search methods and the sorting numbers of the result documents in each search combination set, thereby realizing the integration of these result documents, so that the result documents after the comprehensive sorting can be fed back later, for example, the result documents after the comprehensive sorting can be fed back to the client, so as to facilitate user viewing.

[0049] It should be noted that there may be multiple specific implementation methods for step S22 in actual applications. For example, in the first method, the weights corresponding to various network search methods may be prioritized and arranged in descending order. When the weights are the same, the result documents may be sorted according to their sorting numbers, thereby achieving comprehensive sorting of the result documents.

[0050] For example, the weight of Baidu search is X1, and its search result set is {(result document a, 1), (result document b, 2), (result document c, 3)}. At this time, the search result set of Baidu search includes 3 result documents, namely result document a, result document b and result document c. Among them, the ranking number of result document a in Baidu search is 1, the ranking number of result document b in Baidu search is 2, and the ranking number of result document c in Baidu search is 3; the weight of GPT large model search method is X2, and its search result set is {(result document o, 1) , (result document p, 2), (result document q, 3), (result document r, 4)}, at this time, the search result set of the GPT large model search method includes 4 result documents, namely result document o, result document p, result document q and result document r, among which, the sorting sequence number of result document o in the GPT large model search method is 1, the sorting sequence number of result document p in the GPT large model search method is 2, the sorting sequence number of result document q in the GPT large model search method is 3, and the sorting sequence number of result document r in the GPT large model search method is 4.

[0051] According to the first implementation method, since the weights corresponding to the network search methods are compared first, if the Baidu search weight X1 is greater than the GPT large model search weight X2, the search result set {(result document a, 1), (result document b, 2), (result document c, 3)} can be placed before the search result set {(result document o, 1), (result document p, 2), (result document q, 3), (result document r, 4)}. In addition, the result document a, result document b and result document c have the same weight, so they are sorted according to their sorting numbers, thereby comprehensively sorting the result documents in these search combination sets, and the results of the comprehensive sorting are result document a, result document b, result document c, result document o, result document p, result document q, result document r.

[0052] Among them, the first method gives priority to the weight of the network search method corresponding to the result document, and then considers its own sorting number, and then combines the two to achieve comprehensive sorting of each result document.

[0053] For step S22, method 2 can also be used to implement it. In method 2, the weight of the network search method corresponding to the result document is not given priority, and then the sorting number of the result document is considered. Instead, the sorting number of the result document is converted into a corresponding score (wherein, since the sorting number reflects the importance of the result document in the corresponding network search method, the score is called an importance score), and then combined with the weight of the corresponding network search method, a comprehensive calculation is made for sorting.

[0054] For the specific implementation method of the second method, the importance score of the result document can be determined for each result document in each search combination set according to the sorting sequence number of the result document in the corresponding network search method; then, for each result document in each search combination set, the comprehensive score of the result document can be determined according to the importance score of the result document and the weight of the network search method corresponding to the result document; then, according to the comprehensive scores of the result documents in each search combination set, each result document can be comprehensively sorted, for example, the result document with a larger comprehensive score has a higher comprehensive ranking, thereby realizing the comprehensive sorting, that is, each result document can be comprehensively sorted according to the order of the comprehensive scores from large to small.

[0055] For example, for the result documents in each search combination set, these result documents can be taken as current documents in turn. At this time, the sorting number of the current document in the corresponding network search method can be obtained, and then the importance score of the current result document can be determined according to the sorting number. For example, according to preset rules, the sorting number can be converted into the importance score of the current result document, wherein, since the smaller the sorting number, the higher the importance of the current result document, the larger the corresponding importance score, the sorting number and the importance score are negatively correlated. Therefore, the preset rule reflects the negative correlation between the sorting number and the importance score.

[0056] For example, the preset rule may be m=1 / n, where m is the importance score of the current result document and n is the ranking number of the current document in the corresponding network search method. Of course, the preset rule may also be other types of functions, which are not specifically limited here. In this way, after obtaining the importance score of the current result document, the comprehensive score of the current result document may be further determined based on the importance score of the current result document and the weight of the network search method corresponding to the current result document.

[0057] Among them, for each result document in each search combination set, the comprehensive score of the result document is determined according to the importance score of the result document and the weight of the network search method corresponding to the result document. As for the specific method of determining the comprehensive score, for example, for each result document in each search combination set, the importance score of the result document and the weight of the network search method corresponding to the result document can be multiplied to obtain the comprehensive score of the result document.

[0058] For example, in the above example, when determining the comprehensive score of the current result document based on the importance score of the current result document and the weight of the network search method corresponding to the current result document, the importance score of the current result document can be multiplied by the weight of the network search method corresponding to the current result document, and the product is used as the comprehensive score of the current result document. In this way, the result documents in each search combination set can be used as the current result document in turn, and then the comprehensive score is calculated.

[0059] Step S23: Feedback each result document after comprehensive sorting.

[0060] After the comprehensive sorting of the result documents in each search combination set is achieved through the above-mentioned step S22, in this step S23, each result document after the comprehensive sorting can be fed back, for example, each result document after the comprehensive sorting can be fed back to the client, so as to facilitate user viewing.

[0061] The method provided by the embodiment of the present application includes obtaining the search result sets obtained by various network search methods, and then comprehensively sorting the result documents in each search combination set according to the weights corresponding to the various network search methods and the sorting sequence numbers of the result documents in each search combination set, and then feeding back the comprehensively sorted result documents. In this method, since the search result sets of multiple network search methods are obtained, and the result documents in these search result sets are comprehensively sorted according to the weights corresponding to the various network search methods and the sorting sequence numbers of the result documents in each search combination set, the sorted result documents fed back include the search results of various network search methods, and the search results of these network search methods are integrated through the comprehensive sorting. Therefore, compared with the current single network search method, the search results are more accurate and effective, and the problems in the prior art are solved.

[0062] As mentioned above, for the result documents in each search combination set, the comprehensive score of the result document can be determined based on the importance score of the result document and the weight of the network search method corresponding to the result document. The importance score of the result document can be directly multiplied by the weight of the network search method corresponding to the result document to obtain the comprehensive score of the result document. This is further explained in detail using the current result document as an example.

[0063] However, in practical applications, some special scenarios are usually encountered, such as a result document exists in at least two search combination sets, for example, the search combination sets fed back by two different network search methods each include a result document. At this time, for this special scenario, for each result document in each search combination set, the comprehensive score of the result document is determined according to the importance score of the result document and the weight of the network search method corresponding to the result document. After finally obtaining the comprehensive score of the result document in each search combination set, the method can further perform deduplication in the following manner: for each result document in each search combination set, determine whether the result document exists in at least two search combination sets. If not, no processing is required; on the contrary, if yes, in the case where such a result document exists in at least two search combination sets, the maximum comprehensive score of the result document can be further obtained, that is, in the above manner, since the result document exists in at least two search combination sets, the result document obtains multiple comprehensive scores, and the maximum value thereof can be obtained as the maximum comprehensive score, and then the maximum comprehensive score is multiplied by the magnification factor as the comprehensive score of the result document.

[0064] Among them, the amplification factor is greater than 1, for example, it can be 1.1, 1.2 or other values. For example, if the result document exists in a larger number of search combination sets, it means that more network search methods have obtained the result document through the search terms, which means that the importance of the result document is higher. At this time, the maximum comprehensive score of the result document can be multiplied by the amplification factor to obtain the comprehensive score of the result document.

[0065] It should be further explained that the method provided in the embodiment of the present application needs to use the network search method to set corresponding weights when determining the comprehensive score of the result document, and it is also mentioned above that corresponding weights are set for various network search methods. Here, how to set the weights of the network search methods can be explained in detail.

[0066] In the method provided in the embodiment of the present application, the weights corresponding to the various network search methods can be determined according to the user activities and the proportions of positive and negative feedback corresponding to the various network search methods.

[0067] For example, for a certain network search method, the user activity and the proportion of positive feedback of the network search method can be obtained first, and then the weight corresponding to the network search method can be determined based on the user activity and the proportion of positive feedback of the network search method.

[0068] In actual applications, after a user searches using a certain network search method, he or she can give positive or negative feedback based on the search results of the search method. At this time, the positive feedback and negative feedback respectively reflect the user's evaluation of the use of the network search method. Therefore, for the network search method, the proportion of positive feedback from a large number of users during use can be counted. For example, the proportion of positive feedback from various network search methods during the use of 1 million users can be counted; corresponding to user activity, it can be evaluated by the number of active users. For example, for a certain network search method, its user activity can be characterized by the number of active users of the network search method. For example, the user activity can be the active user multiplied by the activity coefficient (the activity coefficient is greater than 1, such as 5, 10 or other values). In this way, the user activity of various network search methods can be obtained. Among them, the active users of a certain network search method can be users who have used the network search method in the past week (or month).

[0069] In this way, the above method can be used to obtain the proportion of positive feedback of various network search methods and the user activity of various network search methods, and then the weights corresponding to various network search methods can be calculated based on this. For example, the weights corresponding to various network search methods can be calculated by Formula 1 as shown below.

[0070] X=0+n / NFormula 1

[0071] In Formula 1, X is the calculated weight corresponding to the network search method; θ is the proportion of positive feedback of the network search method; n is the user activity of the network search method; and N is the sum of the user activities of various network search methods.

[0072] Of course, in actual applications, the user activity and the proportion of positive feedback of the network search method usually change dynamically, so the weight of the network search method can be periodically updated according to the dynamically changing user activity and the proportion of positive feedback of the network search method.

[0073] Based on the same inventive concept as the network search method provided in the embodiment of the present application, the embodiment of the present application also provides a network search device. If there is any unclear point, please refer to the corresponding content of the method embodiment. Figure 3 FIG. 3 is a schematic diagram of a specific structure of the device 30, which includes: an acquisition unit 301, a comprehensive sorting unit 302, and a feedback unit 303, wherein:

[0074] The acquisition unit 301 is used to acquire search result sets obtained by multiple network search methods respectively, wherein each search combination set includes multiple result documents obtained by searching the corresponding network search method and the sorting sequence number of each result document in the corresponding network search method;

[0075] Comprehensive sorting unit 302, used to comprehensively sort the result documents in each search combination set according to the weights corresponding to each network search method and the sorting sequence number of the result documents in each search combination set;

[0076] The feedback unit 303 is used to feed back each result document after comprehensive sorting.

[0077] The device 30 provided in the embodiment of the present application is adopted. Since the device 30 adopts the same inventive concept as the method provided in the embodiment of the present application, on the premise that the method can solve the technical problem, the device 30 can also solve the technical problem, which will not be repeated here.

[0078] According to the weights corresponding to the various network search methods and the sorting numbers of the result documents in each search combination set, the result documents in each search combination set are comprehensively sorted, which may specifically include:

[0079] For each result document in each search combination set, determine the importance score of the result document according to the ranking sequence number of the result document in the corresponding network search method;

[0080] For each result document in each search combination set, determine a comprehensive score of the result document according to the importance score of the result document and the weight of the network search method corresponding to the result document;

[0081] According to the comprehensive scores of the result documents in each search combination, each result document is comprehensively ranked.

[0082] Wherein, for each result document in each search combination set, determining the comprehensive score of the result document according to the importance score of the result document and the weight of the network search method corresponding to the result document may specifically include:

[0083] For each result document in each search combination set, the importance score of the result document is multiplied by the weight of the network search method corresponding to the result document to obtain a comprehensive score of the result document.

[0084] The device 30 may also include a correction unit for determining, for each search combination set, whether the result document exists in at least two search combination sets; when the result document exists in at least two search combination sets, obtaining the maximum comprehensive score of the result document, and multiplying the maximum comprehensive score by a magnification factor as the comprehensive score of the result document.

[0085] The device 30 may further include a weight determination unit for determining the weights corresponding to the various network search methods according to the user activities and the proportions of positive feedback corresponding to the various network search methods.

[0086] Among them, obtaining the search result sets obtained by multiple network search methods respectively can specifically include: obtaining the search terms input by the user; inputting the search terms into the input interfaces of the multiple network search methods to obtain the search result sets respectively fed back by the multiple network search methods.

[0087] The embodiment of the present invention further provides a storage medium, including: a program, which, when running on an electronic device, enables the electronic device to execute all or part of the process of the method in the above embodiment. The storage medium may be a disk, an optical disk, a read-only memory (ROM), a random access memory (RAM), a flash memory, a hard disk (HDD) or a solid-state drive (SSD). The storage medium may also include a combination of the above types of memories.

[0088] Although the embodiments of the present invention have been described in conjunction with the accompanying drawings, those skilled in the art may make various modifications and variations without departing from the spirit and scope of the present invention, and such modifications and variations are all within the scope defined by the appended claims.

Claims

1. A network search method, characterized in that: The method comprises: Obtaining search result sets obtained by multiple network search methods, wherein each search combination set includes multiple result documents obtained by searching the corresponding network search method, and the sorting sequence number of each result document in the corresponding network search method; According to the weights corresponding to various network search methods and the sorting numbers of the result documents in each search combination set, the result documents in each search combination set are comprehensively sorted; Feedback each result document after comprehensive sorting.

2. The method according to claim 1, characterized in that According to the weights corresponding to various network search methods and the sorting numbers of the result documents in each search combination set, the result documents in each search combination set are comprehensively sorted, including: For each result document in each search combination set, determine the importance score of the result document according to the ranking sequence number of the result document in the corresponding network search method; For each result document in each search combination set, determine a comprehensive score of the result document according to the importance score of the result document and the weight of the network search method corresponding to the result document; According to the comprehensive scores of the result documents in each search combination, each result document is comprehensively ranked.

3. The method according to claim 2, characterized in that For each result document in each search combination set, a comprehensive score of the result document is determined according to the importance score of the result document and the weight of the network search method corresponding to the result document, specifically including: For each result document in each search combination set, the importance score of the result document is multiplied by the weight of the network search method corresponding to the result document to obtain a comprehensive score of the result document.

4. The method according to claim 3, characterized in that The method further comprises: For each result document in each search combination set, determining whether the result document exists in at least two search combination sets; In the case that the result document exists in at least two search combination sets, the maximum comprehensive score of the result document is obtained, and the maximum comprehensive score is multiplied by the magnification factor to serve as the comprehensive score of the result document.

5. The method according to claim 1, characterized in that The method further comprises: The weights corresponding to various network search methods are determined according to the user activity and the proportion of positive feedback corresponding to each network search method.

6. The method according to claim 1, characterized in that Get the search results of various network search methods, including: Get the search term entered by the user; The search term is input into the input interface of multiple network search methods to obtain the search result sets respectively fed back by the multiple network search methods.

7. An electronic device, characterized in that: include: Memory for storing computer programs; A processor, configured to execute the method according to any one of claims 1 to 6.

8. A storage medium, characterized in that: The invention comprises: a program, which, when running on an electronic device, enables the electronic device to execute the method according to any one of claims 1 to 6.

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