Matching methods, computer equipment, and media for virtual reality works

Abstract

The present application provides a method, computer device, and medium for matching virtual reality works. The method includes: obtaining the current user's historical learning information, historical examination information, and historical work browsing information; predicting keywords for the virtual reality works desired by the current user based on the historical learning information, historical examination information, and historical work browsing information to obtain target keywords; determining a target virtual reality work with the target keywords from multiple preset virtual reality works; and outputting the target virtual reality work. The present application predicts keywords for the virtual reality works desired by the current user based on the current user's historical learning information, historical examination information, and historical work browsing information, thereby determining the corresponding target virtual reality work. This avoids the user having to manually search through a large number of interior design works one by one, thereby improving the efficiency of searching for interior design works.

Description

Technical Field

[0001] This application relates to the field of virtual reality works technology, specifically to a method for matching virtual reality works, computer equipment, and media. Background Art

[0002] In industries such as architectural design, platforms can be provided for experiencing and learning interior design. Users can learn about and appreciate the concepts and styles of outstanding interior design works on these platforms to improve their interior design skills. Interior design works are often presented in the form of Virtual Reality (VR), allowing users to experience interior design works in a concrete and visual way within a virtual reality space, making the learning process more intuitive.

[0003] However, these platforms typically collect a large number of interior design works, requiring users to manually search through them one by one to find designs that meet their needs. This demonstrates that the search efficiency for interior design works is extremely low. Summary of the Invention

[0004] The embodiments of this application provide a method, computer device, and medium for matching virtual reality works, which aims to avoid users having to manually search through a large number of interior design works one by one, thereby improving the efficiency of searching for interior design works.

[0005] In a first aspect, embodiments of this application provide a method for matching virtual reality works, the method comprising:

[0006] Retrieve the current user's historical learning information, historical exam information, and historical works browsing information;

[0007] Based on the historical learning information, the historical exam information, and the historical works browsing information, the keywords of the virtual reality works needed by the current user are predicted to obtain the target keywords;

[0008] Among multiple preset virtual reality works, identify the target virtual reality work that contains the target keywords;

[0009] Output the target virtual reality work.

[0010] In some embodiments, the historical learning information includes a first knowledge point learned by the current user during a historical period; and / or

[0011] The historical exam information includes the second knowledge point in the current user's exam during the historical period, and the third knowledge point in the second knowledge point in which the current user answered incorrectly; and / or

[0012] The historical works browsing information includes preset keywords of preset virtual reality works viewed by the current user during the historical period.

[0013] In some embodiments, predicting keywords for virtual reality works needed by the current user based on the historical learning information, the historical exam information, and the historical works browsing information to obtain target keywords includes:

[0014] Identify the first keyword among multiple first knowledge points, the second keyword among multiple second knowledge points, and the third keyword among multiple third knowledge points;

[0015] The target keyword is determined based on the first keyword, the second keyword, the third keyword, and the preset keyword.

[0016] In some embodiments, determining the target keyword based on the first keyword, the second keyword, the third keyword, and the preset keyword includes:

[0017] Multiple first keywords, multiple second keywords, multiple third keywords, and multiple preset keywords are deduplicated to obtain multiple fourth keywords;

[0018] For each of the fourth keywords, determine the number of times the fourth keyword appears in the plurality of first keywords, the plurality of second keywords, the plurality of third keywords, and the plurality of preset keywords;

[0019] Based on the frequency of occurrence, the target keyword is determined from among the multiple fourth keywords.

[0020] In some embodiments, determining the target keyword from among the plurality of fourth keywords based on the frequency of occurrence includes:

[0021] Among the multiple fourth keywords, multiple fifth keywords are determined, wherein at least two of the occurrence counts of the fifth keywords in the multiple first keywords, multiple second keywords, multiple third keywords and multiple preset keywords are not zero;

[0022] Based on the frequency of occurrence, the target keyword is determined from among the multiple fifth keywords.

[0023] In some embodiments, determining the target keyword from among a plurality of fifth keywords based on the frequency of occurrence includes:

[0024] For each of the fifth keywords, the number of times the fifth keyword appears in a plurality of first keywords, a plurality of second keywords, a plurality of third keywords, and a plurality of preset keywords is weighted and summed to obtain the total number of occurrences;

[0025] Based on the overall frequency of occurrence, the target keyword is determined from among the multiple fifth keywords.

[0026] In some embodiments, the weight of the fifth keyword when performing a weighted summation of the occurrence counts of the fifth keyword among multiple first keywords, multiple second keywords, multiple third keywords, and multiple preset keywords is determined in the following manner:

[0027] Determine the first historical moment of the most recently learned first knowledge point in the historical learning information;

[0028] Determine the second historical moment of the second knowledge point in the most recent exam from the historical exam information;

[0029] Determine the third historical moment of the most recent incorrect answer to the third knowledge point in the historical examination information;

[0030] Determine the fourth historical moment of the preset virtual reality work that was most recently viewed in the historical work browsing information;

[0031] Based on the chronological order of the first historical moment, the second historical moment, the third historical moment, and the fourth historical moment, different weights are assigned to the frequency of occurrence of the fifth keyword among multiple first keywords, multiple second keywords, multiple third keywords, and multiple preset keywords.

[0032] In some embodiments, the target virtual reality work is a virtual reality work of interior design, and the target keywords include at least one of interior design style and interior design element.

[0033] Secondly, embodiments of this application provide a matching device for virtual reality works, the matching device for virtual reality works comprising:

[0034] The acquisition module is used to acquire the current user's historical learning information, historical exam information, and historical works browsing information;

[0035] The prediction module is used to predict the keywords of the virtual reality works needed by the current user based on the historical learning information, the historical exam information, and the historical works browsing information, so as to obtain the target keywords;

[0036] The determination module is used to identify a target virtual reality work that has the target keyword from a plurality of preset virtual reality works;

[0037] The output module is used to output the target virtual reality work.

[0038] Thirdly, embodiments of this application provide a computer device including a processor and a memory, the memory storing a computer program configured to be executed by the processor to implement the matching method for virtual reality works as described in any of the preceding claims.

[0039] Fourthly, embodiments of this application provide a computer-readable storage medium storing a computer program configured to be executed by a processor to implement the matching method for virtual reality works as described in any of the preceding claims.

[0040] Fifthly, embodiments of this application provide a computer program product, including a computer program or instructions, which are executed by a processor to implement the matching method for virtual reality works as described in any of the preceding claims.

[0041] The beneficial effects of the embodiments of this application are as follows:

[0042] In the embodiments of this application, keywords for virtual reality works needed by the current user are predicted based on the current user's historical learning information, historical exam information, and historical work browsing information, thereby determining the corresponding target virtual reality works. This avoids the user having to manually search through a large number of interior design works one by one, thus improving the search efficiency of interior design works. Attached Figure Description

[0043] To more clearly illustrate the technical solutions in the embodiments of this application, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0044] Figure 1 This is a schematic flowchart of an embodiment of the virtual reality works matching method provided in this application;

[0045] Figure 2 This is a schematic flowchart of another embodiment of the virtual reality works matching method provided in this application;

[0046] Figure 3 This is a schematic flowchart of another embodiment of the virtual reality works matching method provided in the embodiments of this application;

[0047] Figure 4 This is a schematic flowchart of another embodiment of the virtual reality works matching method provided in this application;

[0048] Figure 5 This is a schematic diagram of an embodiment of the computer device provided in this application. Detailed Implementation

[0049] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0050] In the description of this application, "a plurality of" means two or more, unless otherwise explicitly specified. Furthermore, in the description of this application, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of indicated technical features. Therefore, a feature defined as "first" or "second" may explicitly or implicitly include one or more of the stated features.

[0051] Firstly, embodiments of this application provide a method for matching virtual reality works. Specifically, refer to... Figure 1 , Figure 1 This is a schematic flowchart illustrating one embodiment of a method for matching virtual reality works. Figure 1 The matching method for this virtual reality work may include:

[0052] 101. Obtain the current user's historical learning information, historical exam information, and historical works browsing information.

[0053] In the embodiments of this application, the current user refers to the user currently logged into the experience and learning platform that provides content such as interior design. This platform is connected to the learning, practice, and assessment platform for interior design content to obtain the current user's historical learning and exam information from the learning, practice, and assessment platform for interior design content. The current user's historical work browsing information can be directly obtained from the experience and learning platform that provides content such as interior design. The experience and learning platform that provides content such as interior design can have multiple preset virtual reality (VR) works set up for users to learn from and appreciate.

[0054] In the embodiments of this application, the current user's historical learning information refers to the current user's historical learning records on topics such as interior design. The current user's historical examination information refers to the current user's historical examination records on topics such as interior design. The current user's historical work browsing information refers to the current user's browsing records of preset virtual reality works.

[0055] 102. Based on historical learning information, historical exam information, and historical work browsing information, predict the keywords of the virtual reality works currently needed by the user to obtain the target keywords.

[0056] In the embodiments of this application, since it is necessary to predict the virtual reality works currently needed by the user, the keywords of the virtual reality works currently needed by the user can be predicted first to obtain the target keywords. Historical learning information, historical exam information, and historical work browsing information represent the user's historical needs for content such as interior design. Therefore, based on historical learning information, historical exam information, and historical work browsing information, the keywords of the virtual reality works currently needed by the user can be predicted, making the prediction of the virtual reality works currently needed by the user more accurate.

[0057] It is understandable that users may have doubts or misunderstandings during the process of learning history, taking history exams, and browsing historical works. Therefore, they may need to further understand and consolidate the relevant content. Thus, predicting the keywords of the virtual reality works that users need based on historical learning information, historical exam information, and historical work browsing information can match the predicted target keywords with the user's needs.

[0058] 103. Among multiple preset virtual reality works, identify the target virtual reality work that has the target keywords.

[0059] In the embodiments of this application, at least one preset keyword is pre-set for each preset virtual reality work. The preset keyword can be determined based on the interior design content in the preset virtual reality work. Therefore, among multiple preset virtual reality works, the preset virtual reality work with the same preset keyword as the target keyword can be regarded as the target virtual reality work with the target keyword.

[0060] In some embodiments of this application, taking a virtual reality work that is an interior design as an example, the target keywords include at least one of interior design style and interior design elements. Interior design style may include, for example, modern style, cream style, Nordic style, natural wood style, etc. Interior design elements may include, for example, ceilings, doors and windows, tables and chairs, wall decorations, floor decorations, and the area range of the interior design, etc.

[0061] The interior design area range can be a preset area range encompassing the floor area of ​​the interior space. Alternatively, it can be a preset area range encompassing the area to be designed (including at least one of the floor area, wall area, and ceiling area).

[0062] 104. Output the target virtual reality work.

[0063] In the embodiments of this application, when the current user logs into an experience and learning platform that provides content such as interior design, at least one target virtual reality work can be displayed in the platform's preset work recommendation area so that the current user can quickly identify the virtual reality work they need.

[0064] As can be seen, in the above embodiments of this application, the keywords of the virtual reality works required by the current user are predicted by the current user's historical learning information, historical exam information and historical work browsing information, thereby determining the corresponding target virtual reality works, avoiding the user having to manually search through a large number of interior design works one by one, thus improving the search efficiency of interior design works.

[0065] In some embodiments of this application, the historical learning information, historical exam information, and historical work browsing information of the current user are described in detail. Specifically, this includes at least one of the following:

[0066] The historical learning information includes the first knowledge point that the current user learned in the historical period;

[0067] The historical exam information includes the second knowledge point in the current user's exam during the historical period, and the third knowledge point in the second knowledge point in which the current user answered incorrectly;

[0068] The browsing information for historical works includes preset keywords for the virtual reality works viewed by the current user during the historical period.

[0069] It is understandable that the first, second, and third knowledge points can all be knowledge points in the content of interior design, such as the construction process of dry hanging stone on walls, the construction process of wood veneer on walls, and the construction of door and window decoration projects.

[0070] In some embodiments of this application, such as Figure 2 As shown, in Figure 1 Based on the illustrated embodiment, and according to historical learning information, historical exam information, and historical work browsing information, the keywords for the virtual reality works currently needed by the user are predicted to obtain target keywords, which may include:

[0071] 201. Identify the first keyword among multiple first knowledge points, the second keyword among multiple second knowledge points, and the third keyword among multiple third knowledge points.

[0072] In the embodiments of this application, the first knowledge point, the second knowledge point, and the third knowledge point all include corresponding keywords, such as at least one of interior design style and interior design element.

[0073] 202. Based on the first keyword, second keyword, third keyword, and preset keywords, determine the target keywords.

[0074] In the embodiments of this application, since at least one of the first keyword, the second keyword, the third keyword and the preset keyword matches the user's needs, at least one target keyword can be selected from the first keyword, the second keyword, the third keyword and the preset keyword.

[0075] In some embodiments of this application, the selection of target keywords can be determined based on the frequency of keyword occurrence. Specifically, determining target keywords based on first keywords, second keywords, third keywords, and preset keywords can include: deduplicating multiple first keywords, multiple second keywords, multiple third keywords, and multiple preset keywords to obtain multiple fourth keywords; for each fourth keyword, determining the frequency of occurrence of the fourth keyword among the multiple first keywords, multiple second keywords, multiple third keywords, and multiple preset keywords; and determining the target keyword among the multiple fourth keywords based on the frequency of occurrence. It can be seen that, based on the frequency of occurrence, target keywords that better match user needs can be determined, thereby improving the accuracy of matching target virtual reality works.

[0076] In some embodiments of this application, such as Figure 3 As shown, in Figure 1 or Figure 2 Based on the illustrated embodiment, the target keyword is determined from multiple fourth keywords based on the frequency of occurrence, which may include:

[0077] 301. Among multiple fourth keywords, multiple fifth keywords are identified, wherein at least two of the occurrence counts of the fifth keywords in multiple first keywords, multiple second keywords, multiple third keywords, and multiple preset keywords are not zero.

[0078] In the embodiments of this application, the requirement that at least two occurrence counts are not zero refers to the following four occurrence counts: the occurrence count of the fifth keyword among multiple first keywords, the occurrence count of the fifth keyword among multiple second keywords, the occurrence count of the fifth keyword among multiple third keywords, and the occurrence count of the fifth keyword among multiple preset keywords. If at least two occurrence counts are zero, it indicates that the user's demand for a corresponding fourth keyword is low, and therefore it can be excluded as a fifth keyword, thus achieving preliminary filtering of the fourth keyword.

[0079] 302. Based on the frequency of occurrence, the target keyword is determined from multiple fifth keywords.

[0080] In the embodiments of this application, based on the frequency of occurrence, a target keyword that is more compatible with the user's needs can be determined from among multiple fifth keywords.

[0081] In some embodiments of this application, determining the target keyword from multiple fifth keywords based on the frequency of occurrence may include: for each fifth keyword, performing a weighted summation of the frequency of occurrence of the fifth keyword among multiple first keywords, multiple second keywords, multiple third keywords, and multiple preset keywords to obtain a comprehensive frequency of occurrence; and determining the target keyword from multiple fifth keywords based on the comprehensive frequency of occurrence. It can be seen that by using weighted summation, a more accurate comprehensive frequency of occurrence can be obtained, thus making the determined target keyword more in line with user needs.

[0082] In some embodiments of this application, such as Figure 4 As shown, in Figures 1 to 3 Based on any of the embodiments shown, the weight of the fifth keyword when performing a weighted summation of the frequency of occurrence among multiple first keywords, multiple second keywords, multiple third keywords, and multiple preset keywords can be determined in the following way:

[0083] 401. Determine the first historical moment of the most recently learned first knowledge point in the historical learning information.

[0084] In the embodiments of this application, since each first knowledge point in the historical learning information corresponds to a corresponding user learning time, the user learning time closest to the current time point can be determined among multiple user learning times and used as the first historical time of the most recently learned first knowledge point in the historical learning information.

[0085] 402. Determine the second historical moment of the second knowledge point in the most recent exam in the historical exam information.

[0086] In the embodiments of this application, since each second knowledge point in the historical examination information corresponds to a corresponding user examination time, the user examination time closest to the current time point can be determined among the user examination times of multiple second knowledge points, and used as the second historical time of the second knowledge point of the most recent examination in the historical examination information.

[0087] 403. Determine the third historical moment of the third knowledge point in the historical exam information where the most recent answer was incorrect.

[0088] In the embodiments of this application, since each third knowledge point in the historical exam information corresponds to a corresponding user exam time, the user exam time closest to the current time point can be determined among the user exam times of multiple third knowledge points, and used as the third historical time of the third knowledge point that was most recently answered incorrectly in the historical exam information.

[0089] 404. Determine the fourth historical moment of the most recently viewed preset virtual reality work in the historical works browsing information.

[0090] In the embodiments of this application, since each preset virtual reality work in the historical work browsing information can correspond to a corresponding user browsing time, the user browsing time closest to the current time can be determined among multiple user browsing times and used as the fourth historical time of the preset virtual reality work most recently viewed in the historical work browsing information.

[0091] 405. Based on the chronological order of the first, second, third, and fourth historical moments, different weights are assigned to the frequency of occurrence of the fifth keyword among multiple first keywords, multiple second keywords, multiple third keywords, and multiple preset keywords.

[0092] In the embodiments of this application, in the chronological order, the fifth keyword, which is closer to the current historical moment, has a greater weight in the number of times it appears among multiple corresponding keywords. Taking the chronological order of the first, second, third, and fourth historical moments as an example, the weight of the fifth keyword's appearance among multiple first keywords, the weight of the fifth keyword's appearance among multiple second keywords, the weight of the fifth keyword's appearance among multiple third keywords, and the weight of the fifth keyword's appearance among multiple preset keywords decrease sequentially. In this way, by weighted summation, a more accurate comprehensive appearance count can be obtained, thereby making the determined target keywords more in line with user needs.

[0093] Secondly, based on the virtual reality works matching method of the above embodiments, embodiments of this application provide a virtual reality works matching device, which is used to execute the steps of any embodiment of the virtual reality works matching method described above. Specifically, the virtual reality works matching device may include:

[0094] The acquisition module is used to acquire the current user's historical learning information, historical exam information, and historical works browsing information;

[0095] The prediction module is used to predict the keywords of the virtual reality works needed by the current user based on historical learning information, historical exam information, and historical work browsing information, and obtain the target keywords.

[0096] The determination module is used to identify target virtual reality works with target keywords from multiple preset virtual reality works;

[0097] The output module is used to output the target virtual reality artwork.

[0098] Thirdly, embodiments of this application provide a computer device that integrates any of the virtual reality work matching devices provided in the embodiments of this application. The computer device includes a processor and a memory, the memory storing a computer program configured to be executed by the processor to implement the virtual reality work matching method as described in any of the above embodiments, for example:

[0099] Obtain the current user's historical learning information, historical exam information, and historical work browsing information; based on the historical learning information, historical exam information, and historical work browsing information, predict the keywords of the virtual reality works required by the current user to obtain target keywords; among multiple preset virtual reality works, determine the target virtual reality works that have the target keywords; output the target virtual reality works.

[0100] Fourthly, embodiments of this application provide a computer device that integrates a matching device for any of the virtual reality works provided in embodiments of this application. For example... Figure 5 As shown, it illustrates a structural schematic diagram of the computer device involved in the embodiments of this application, specifically:

[0101] The computer device may include components such as a processor 501 with one or more processing cores, a storage unit 502 with one or more computer-readable storage media, a power supply 503, and an input unit 504. Those skilled in the art will understand that... Figure 5 The computer device structure shown does not constitute a limitation on the computer device and may include more or fewer components than shown, or combine certain components, or have different component arrangements. Wherein:

[0102] The processor 501 is the control center of the computer device. It connects various parts of the computer device via various interfaces and lines, and performs various functions and processes data by running or executing software programs and / or modules stored in the storage unit 502, and by calling data stored in the storage unit 502, thereby providing overall monitoring of the computer device. Optionally, the processor 501 may include one or more processing cores; preferably, the processor 501 may integrate an application processor and a modem processor, wherein the application processor mainly handles the operating system, user interface, and applications, and the modem processor mainly handles wireless communication. It is understood that the modem processor may also not be integrated into the processor 501.

[0103] Storage unit 502 can be used to store software programs and modules. Processor 501 executes various functional applications and data processing by running the software programs and modules stored in storage unit 502. Storage unit 502 may mainly include a program storage area and a data storage area. The program storage area may store the operating system, application programs required for at least one function (such as sound playback function, image playback function, etc.), etc.; the data storage area may store data created according to the use of the computer device, etc. In addition, storage unit 502 may include high-speed random access memory, and may also include non-volatile memory, such as at least one disk storage device, flash memory device, or other volatile solid-state storage device. Accordingly, storage unit 502 may also include a memory controller to provide processor 501 with access to storage unit 502.

[0104] The computer equipment also includes a power supply 503 that supplies power to the various components. Preferably, the power supply 503 can be logically connected to the processor 501 through a power management system, thereby enabling functions such as charging, discharging, and power consumption management through the power management system. The power supply 503 may also include one or more DC or AC power supplies, recharging systems, power fault detection circuits, power converters or inverters, power status indicators, and other arbitrary components.

[0105] The computer device may also include an input unit 504, which can be used to receive input digital or character information and generate keyboard, mouse, joystick, optical or trackball signal inputs related to user settings and function control.

[0106] Although not shown, the computer device may also include a display unit, etc., which will not be described in detail here. Specifically, in the embodiments of this application, the processor 501 in the computer device loads the executable files corresponding to the processes of one or more application programs into the storage unit 502 according to the following instructions, and the processor 501 runs the application programs stored in the storage unit 502 to realize various functions, such as:

[0107] Obtain the current user's historical learning information, historical exam information, and historical work browsing information; based on the historical learning information, historical exam information, and historical work browsing information, predict the keywords of the virtual reality works required by the current user to obtain target keywords; among multiple preset virtual reality works, determine the target virtual reality works that have the target keywords; output the target virtual reality works.

[0108] Fifthly, embodiments of this application provide a computer-readable storage medium, which may include: read-only memory (ROM), random access memory (RAM), a magnetic disk, or an optical disk, etc. The computer-readable storage medium stores a computer program configured to be executed by a processor to implement the matching method for virtual reality works as described in any of the preceding claims, for example:

[0109] Obtain the current user's historical learning information, historical exam information, and historical work browsing information; based on the historical learning information, historical exam information, and historical work browsing information, predict the keywords of the virtual reality works required by the current user to obtain target keywords; among multiple preset virtual reality works, determine the target virtual reality works that have the target keywords; output the target virtual reality works.

[0110] Sixthly, embodiments of this application provide a computer program product or computer program including computer instructions stored in a computer-readable storage medium. A processor of a computer device reads the computer instructions from the computer-readable storage medium and executes the computer instructions, causing the computer device to perform a matching method for virtual reality works as described in any of the preceding claims, for example:

[0111] Obtain the current user's historical learning information, historical exam information, and historical work browsing information; based on the historical learning information, historical exam information, and historical work browsing information, predict the keywords of the virtual reality works required by the current user to obtain target keywords; among multiple preset virtual reality works, determine the target virtual reality works that have the target keywords; output the target virtual reality works.

[0112] The embodiments of this application have been described in detail above. Specific examples have been used to illustrate the principles and implementation methods of this application. The description of the above embodiments is only for the purpose of helping to understand the method and core ideas of this application. At the same time, for those skilled in the art, there will be changes in the specific implementation methods and application scope based on the ideas of this application. Therefore, the content of this specification should not be construed as a limitation of this application.

Claims

1. A method for matching virtual reality works, characterized in that, The matching method for the virtual reality works includes: Retrieve the current user's historical learning information, historical exam information, and historical works browsing information; Based on the historical learning information, the historical exam information, and the historical works browsing information, the keywords of the virtual reality works required by the current user are predicted to obtain target keywords. The target keywords include the range of interior design area, which is a preset area range of the area where the interior needs to be designed. Among multiple preset virtual reality works, a target virtual reality work with the target keyword is identified, wherein the target virtual reality work is a virtual reality work of interior design. Output the target virtual reality work; The historical learning information includes the first knowledge point learned by the current user in a historical period; the historical exam information includes the second knowledge point in the exam of the current user in a historical period, and the third knowledge point in which the current user answered incorrectly; the historical works browsing information includes preset keywords of preset virtual reality works browsed by the current user in a historical period. The target keyword is determined as follows: Multiple fifth keywords are identified from the first keywords in multiple first knowledge points, the second keywords in multiple second knowledge points, and the third keywords in multiple third knowledge points; for each fifth keyword, the frequency of occurrence of the fifth keyword in multiple first keywords, multiple second keywords, multiple third keywords, and multiple preset keywords is weighted and summed to obtain a comprehensive frequency; based on the comprehensive frequency, the target keyword is determined from the multiple fifth keywords. The determination of multiple fifth keywords from the first keywords in multiple first knowledge points, the second keywords in multiple second knowledge points, and the third keywords in multiple third knowledge points includes: Multiple first keywords, multiple second keywords, multiple third keywords, and multiple preset keywords are deduplicated to obtain multiple fourth keywords; For each of the fourth keywords, determine the number of times the fourth keyword appears in the plurality of first keywords, the plurality of second keywords, the plurality of third keywords, and the plurality of preset keywords; Based on the number of times the fourth keyword appears in multiple first keywords, multiple second keywords, multiple third keywords, and multiple preset keywords, multiple fifth keywords are determined from the multiple fourth keywords.

2. The matching method for virtual reality works as described in claim 1, characterized in that, The method of determining multiple fifth keywords from among the multiple fourth keywords based on the frequency of occurrence of the fourth keyword in multiple first keywords, multiple second keywords, multiple third keywords, and multiple preset keywords includes: Among the multiple fourth keywords, multiple fifth keywords are determined, wherein at least two of the occurrence counts of the fifth keywords in the multiple first keywords, multiple second keywords, multiple third keywords, and multiple preset keywords are not zero.

3. The matching method for virtual reality works as described in claim 1, characterized in that, The weight of the fifth keyword when performing a weighted summation of the frequency of occurrences among multiple first keywords, multiple second keywords, multiple third keywords, and multiple preset keywords is determined in the following way: Determine the first historical moment of the most recently learned first knowledge point in the historical learning information; Determine the second historical moment of the second knowledge point in the most recent exam from the historical exam information; Determine the third historical moment of the most recent incorrect answer to the third knowledge point in the historical examination information; Determine the fourth historical moment of the preset virtual reality work that was most recently viewed in the historical work browsing information; Based on the chronological order of the first historical moment, the second historical moment, the third historical moment, and the fourth historical moment, different weights are assigned to the frequency of occurrence of the fifth keyword among multiple first keywords, multiple second keywords, multiple third keywords, and multiple preset keywords.

4. The matching method for virtual reality works as described in any one of claims 1 to 3, characterized in that, The target keywords also include interior design style.

5. A computer device, characterized in that, The computer device includes a processor and a memory, the memory storing a computer program configured to be executed by the processor to implement the matching method for virtual reality works according to any one of claims 1 to 4.

6. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program configured to be executed by a processor to implement the matching method for virtual reality works according to any one of claims 1 to 4.

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