Topic index creation method, computer equipment and computer storage medium
By creating a question index and using the attribute information and feature information of the question data set, students' time consumption is solved for finding specific questions in massive questions, achieving rapid search and review of questions, and improving learning efficiency.
Patent Information
- Application Number
- CN202411295667.9
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-09-14
- Publication Date
- 2024-12-27
AI Technical Summary
When students find specific knowledge points or questions that need to be reviewed in a large number of questions, it consumes a lot of time, which affects learning efficiency.
By obtaining the attribute information and feature information of the question data set, creating a question index, including source information and difficulty scores, users can quickly find specific questions based on the index information, and improve the speed of obtaining specific questions.
Significantly reduce the time for finding questions, so that students can review previous questions or find questions with specific knowledge points, and improve learning efficiency.
Smart Images
Figure CN119202122A_ABST
Abstract
Description
Technical Field
[0001] The embodiments of the present application relate to the field of data retrieval, and specifically to a topic index creation method, a computer device, and a computer storage medium. Background Art
[0002] In the process of learning knowledge, students will do a lot of exercises to consolidate their understanding and memory of knowledge points, and will record questions that they cannot do, questions that they did wrong, or other questions that need special attention. These questions can be recorded in paper notebooks or recorded and stored through electronic devices.
[0003] As knowledge learning continues and deepens, students record more and more questions. When students need to review and revise questions on certain knowledge points, or need to review questions with a difficulty level that matches their own learning situation, it is extremely difficult to find the questions they need to review in a huge amount of questions. This will take a lot of time and affect students' learning efficiency. Summary of the invention
[0004] The embodiments of the present application provide a method for creating a topic index, a computer device, and a computer storage medium, which are used to increase the speed at which students obtain specific topics, reduce the time spent searching for topics, and facilitate students to review previous topics or search for topics on specific knowledge points, thereby improving students' learning efficiency.
[0005] A first aspect of an embodiment of the present application provides a method for creating a topic index, the method being applied to a computer device; the method comprising:
[0006] Acquire a question data set, where the question data set includes a plurality of questions;
[0007] Acquire attribute information and feature information of each question in the question data set, wherein the attribute information includes source information of the question;
[0008] For each question in the question data set, creating an index of the question according to the source information and the feature information of the question, and obtaining index information of the question data set;
[0009] The index information of the topic data set is outputted, so that the user can determine the topic in the topic data set according to the index information of the topic data set.
[0010] A second aspect of an embodiment of the present application provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and the processor implements the method of the first aspect when executing the computer program.
[0011] A third aspect of the embodiments of the present application provides a computer program product. When the computer program product is run on a computer device, the computer device executes the method of the first aspect.
[0012] A fourth aspect of an embodiment of the present application provides a computer storage medium, in which instructions are stored. When the instructions are executed on a computer, the computer executes the method of the first aspect.
[0013] It can be seen from the above technical solutions that the embodiments of the present application have the following advantages:
[0014] The computer device obtains a question data set, and obtains attribute information and feature information of each question in the question data set, the attribute information includes the source information of the question, and for each question in the question data set, creates an index of the question according to the source information and feature information of the question, obtains the index information of the question data set, and outputs the index information of the question data set, so that the user can determine the questions in the question data set according to the index information of the question data set. Therefore, students can directly search for specific questions according to the index information of the question data set, which can significantly improve the speed of obtaining specific questions and reduce the time of searching for questions compared to searching for questions one by one in a large number of questions, and facilitate students to review previous questions or search for questions of specific knowledge points, thereby improving students' learning efficiency. BRIEF DESCRIPTION OF THE DRAWINGS
[0015] Figure 1 This is a schematic diagram of the network framework in the embodiment of the present application;
[0016] Figure 2 A flowchart of a method for creating a topic index in an embodiment of the present application;
[0017] Figure 3 This is an exemplary schematic diagram of the index information of the topic data set in the embodiment of the present application;
[0018] Figure 4 An exemplary schematic diagram of a file printed out by index information of a topic data set in an embodiment of the present application;
[0019] Figure 5 This is an exemplary schematic diagram of feature aggregation index information of a topic data set in an embodiment of the present application;
[0020] Figure 6 It is a structural schematic diagram of a computer device in an embodiment of the present application. DETAILED DESCRIPTION
[0021] The embodiments of the present application provide a method for creating a topic index, a computer device, and a computer storage medium, which are used to increase the speed at which students obtain specific topics, reduce the time spent searching for topics, and facilitate students to review previous topics or search for topics on specific knowledge points, thereby improving students' learning efficiency.
[0022] See also Figure 1 In the embodiment of the present application, the network framework includes:
[0023] The service server 100 and the terminal cluster; the terminal cluster may include: terminal device 200a, terminal device 200b, terminal device 200c, ..., terminal device 200n and other terminal devices.
[0024] The business server 100 may be an independent physical server, a server cluster or a distributed system composed of multiple physical servers, or a cloud server that provides basic cloud computing services such as cloud database, cloud service, cloud computing, cloud function, cloud storage, network service, cloud communication, middleware service, domain name service, security service, CDN, and big data and artificial intelligence platform. The terminal device (including terminal device 200a, terminal device 200b, terminal device 200c, ..., terminal device 200n) may be a smart phone, tablet computer, laptop computer, desktop computer, PDA, mobile internet device (MID), wearable device (such as smart watch, smart bracelet, etc.), smart computer, smart car and other smart terminals.
[0025] Among them, the business server 100 can establish a communication connection with each terminal device in the terminal cluster, and a communication connection can also be established between each terminal device in the terminal cluster. In other words, the business server 100 can establish a communication connection with each terminal device in the terminal device 200a, terminal device 200b, terminal device 200c, ..., terminal device 200n. For example, a communication connection can be established between the terminal device 200a and the business server 100. A communication connection can be established between the terminal device 200a and the terminal device 200b, and a communication connection can also be established between the terminal device 200a and the terminal device 200c. Among them, the above-mentioned communication connection does not limit the connection method, and can be directly or indirectly connected through a wired communication method, or can be directly or indirectly connected through a wireless communication method, etc., which can be determined according to the actual application scenario, and this application is not limited here.
[0026] It should be understood that Figure 1Each terminal device in the terminal cluster shown can be installed with an application client. When the application client runs in each terminal device, it can interact with the business server 100 respectively, so that the business server 100 can receive business data from each terminal device (such as financial management data uploaded by the user through the terminal device). Among them, the application client can be an application client with the function of displaying text, images, audio and video, such as a financial management application, an enterprise affairs management application, a browser application, a social application, an instant messaging application, a live broadcast application, a game application, a short video application, a video application, a music application, a shopping application, a novel application, a payment application, etc., which can be determined according to the actual application scenario requirements and is not limited here. Among them, the application client can be an independent client or an embedded sub-client integrated in a client (such as a financial management client, an enterprise affairs management client, etc.), which can be determined according to the actual application scenario and is not limited here.
[0027] The following will be combined Figure 1 The network framework of the present application describes the topic index creation method in the embodiment of the present application:
[0028] See also Figure 2 In one embodiment of the present application, a method for creating a topic index includes:
[0029] 201. Obtain a question data set, where the question data set includes a plurality of questions;
[0030] The method of this embodiment can be applied to a computer device, which can be Figure 1 The business server 100 or each terminal device in the network framework shown. The computer device can obtain a question data set, which can be multiple questions selected by the user. For example, students record the exercises they do during the learning process, and over time, they record multiple questions to form a question data set. The questions that students choose to record can be questions that they cannot do, questions that they have done wrong, or other questions that need to be focused on. These questions that students choose to record can be called self-selected questions.
[0031] Of course, the question dataset may also come from other sources, such as questions that teachers summarize as students' easy mistakes, questions that students need to focus on, or questions used to expand knowledge points, etc. It may also be multiple questions summarized in other ways, which is not limited here.
[0032] 202. Obtaining attribute information and feature information of each question in the question data set, wherein the attribute information includes source information of the question;
[0033] The computer device can also obtain the attribute information and feature information of each question in the question data set. The attribute information includes the source information of the question, that is, the source of the question. The feature information of the question indicates the characteristics of the question, such as the difficulty of the question, the knowledge points involved in the question, and other information used to describe the characteristics of the question.
[0034] 203. For each question in the question data set, create an index of the question according to the source information and the feature information of the question, and obtain index information of the question data set;
[0035] After obtaining the attribute information and feature information of each question, for each question, an index of the question can be created based on the source information and feature information of the question, that is, the source information and feature information of the question are associated and displayed as the index of the question, and then the index information of the question data set can be obtained.
[0036] 204. Outputting index information of the topic data set, so that the user can determine the topic in the topic data set according to the index information of the topic data set;
[0037] The computer device may output the index information of the question data set, for example, the index information of the question data set may be displayed on a display, or the index information of the question data set may be printed as a paper file for output, which is not limited here. Therefore, by outputting the index information of the question data set, a user such as a student may determine the questions in the question data set according to the index information of the question data set, for example, the question to be viewed may be quickly found in the question data set according to the index of the question.
[0038] In this embodiment, the computer device obtains a question data set, and obtains attribute information and feature information of each question in the question data set, the attribute information includes the source information of the question, for each question in the question data set, creates an index of the question according to the source information and feature information of the question, obtains the index information of the question data set, and outputs the index information of the question data set, so that the user can determine the questions in the question data set according to the index information of the question data set. Therefore, students can directly search for specific questions according to the index information of the question data set, which can significantly improve the speed of obtaining specific questions and reduce the time of searching for questions compared to searching for questions one by one in a large number of questions, and facilitate students to review previous questions or search for questions of specific knowledge points, thereby improving students' learning efficiency.
[0039] based on Figure 2In a preferred implementation manner of the illustrated embodiment, when obtaining the attribute information of each question in a question data set, a specific method may be, for each question in the question data set, obtaining information of a carrier for recording the question, and obtaining the position of the question in the carrier and / or identification information of the question.
[0040] The carrier may be a paper carrier, such as a workbook, a test paper, etc.; or an electronic carrier, such as an electronic document. The position of the question in the carrier, for example, for a paper carrier such as a workbook, may be information indicating the position such as the page number of the question in the workbook, the question number, or the chapter of the question in the workbook; for a paper carrier such as a test paper, may be information indicating the position such as the question number in the test paper; for an electronic carrier such as an electronic document, may be information indicating the position such as the page number of the question in the electronic document, the question number, or the chapter of the question in the electronic document. The identification information of the question may be information used to identify the question, such as the question number of the question.
[0041] Therefore, by using the information of the carrier used to record the question, the position of the question in the carrier and / or the identification information of the question as the index information of the question, the user can quickly locate the carrier where the question is located and the position of the question in the carrier, quickly find the corresponding question, and improve the efficiency of obtaining the question. For students, it can facilitate students to find the required questions and improve learning efficiency.
[0042] based on Figure 2 In the illustrated embodiment, in a preferred implementation manner, when obtaining characteristic information of each question in a question data set, a specific method may be, for each question in the question data set, obtaining a first difficulty score of a question that is the same as or similar to the question, and / or determining a second difficulty score based on the position of the question in a carrier used to record the question, and / or determining a third difficulty score based on the question knowledge type of the question, and / or determining a fourth difficulty score based on the character type of the answer to the question and the number of different character types; determining the difficulty score of the question based on one or more of the first difficulty score, the second difficulty score, the third difficulty score and the fourth difficulty score.
[0043] In which, when determining the difficulty score of the question based on one or more of the first difficulty score, the second difficulty score, the third difficulty score and the fourth difficulty score, a weighted sum of at least two of the first difficulty score, the second difficulty score, the third difficulty score and the fourth difficulty score may be performed, and the sum obtained may be used as the difficulty score of the question.
[0044] For example, when obtaining the first difficulty score of a question that is identical or similar to the question, you can search for questions that are identical or similar to the question on the Internet and obtain the first difficulty score of questions that are identical or similar to the question. If there is no first difficulty score, that is, there is no question that is identical or similar to the question on the Internet or questions that are identical or similar to the question have no difficulty score, then the difficulty score of the question can be determined based on one or more of the second difficulty score, the third difficulty score and the fourth difficulty score.
[0045] When determining the second difficulty score based on the position of the question in the carrier used to record the question, for example, if the question is at the beginning of a chapter, the second difficulty score is determined to be a relatively low score, indicating that the difficulty of the question is relatively low; if the question is in the middle of a chapter, the second difficulty score is determined to be a relatively medium score, indicating that the difficulty of the question is medium; if the question is at the end of a chapter, the second difficulty score is determined to be a relatively high score, indicating that the difficulty of the question is relatively high.
[0046] When determining the third difficulty score according to the knowledge type of the question, for example, if the knowledge type of the question is a basic question, the third difficulty score of the question is determined to be a relatively low score, indicating that the difficulty of the question is low; if the knowledge type of the question is an advanced question, the third difficulty score is determined to be a relatively medium score, indicating that the difficulty of the question is medium; if the knowledge type of the question is an exploratory question, the third difficulty score is determined to be a relatively high score, indicating that the difficulty of the question is high.
[0047] When determining the fourth difficulty score based on the character types and the number of different character types in the answer to the question, if the number of Chinese characters in the answer to the question accounts for a large proportion and the number of letters and numbers accounts for a small proportion, then the fourth difficulty score of the question is determined to be a relatively low score, indicating that the difficulty of the question is low; if the number of Chinese characters in the answer to the question accounts for a medium proportion and the number of letters and numbers accounts for a medium proportion, then the fourth difficulty score of the question is determined to be a relatively medium score, indicating that the difficulty of the question is medium; if the number of Chinese characters in the answer to the question accounts for a small proportion and the number of letters and numbers accounts for a large proportion, then the fourth difficulty score of the question is determined to be a relatively high score, indicating that the difficulty of the question is high.
[0048] Therefore, by displaying the difficulty rating of questions in the index of questions, users such as students can select questions whose difficulty matches their learning situation for review and viewing based on their level of knowledge mastery, which makes it easier for students to review and consolidate knowledge points in a targeted manner and improve learning efficiency.
[0049] based on Figure 2In the illustrated embodiment, in a preferred implementation manner, when obtaining characteristic information of each question in a question data set, a specific method may be to obtain a knowledge point word segmentation library, the knowledge point word segmentation library includes word segments corresponding to each knowledge point name, determine the keywords of the knowledge point in the word segmentation of each knowledge point name, and obtain keywords of multiple knowledge points. For each question in the question data set, perform word segmentation processing on the stem of the question to obtain multiple word segments of the stem of the question, and determine the target keyword that matches the word segmentation of the stem of the question among the keywords of the multiple knowledge points. One question can have multiple keywords.
[0050] The knowledge point word segmentation library stores the word segmentation corresponding to each knowledge point. For example, for the knowledge point "definition of geometry", its word segmentation may include the two word segmentations "geometry" and "definition". The keyword of the knowledge point can be determined from these multiple word segmentations. Obviously, "geometry" is the keyword of the knowledge point. Similarly, the keywords in multiple word segmentations of each knowledge point can be determined. A knowledge point can have only one keyword or multiple keywords.
[0051] For example, the word segmentations of the question stem include "rectangle", "shaded area" and other word segmentations, which have a high degree of matching with the keyword "geometry". Therefore, it can be determined that the target keyword corresponding to the question is "geometry". The target keyword can be written into the index of the question, so that the user can know that the question involves the knowledge point of "geometry".
[0052] Therefore, the stem of the question is segmented to obtain multiple segmentations of the stem of the question, and the target keyword that matches the segmentation of the stem of the question is determined among the keywords of multiple knowledge points. The target keyword can be written into the index of the question, so that the user can know the knowledge points involved in the question, which is convenient for the user to find questions about specific knowledge points, and convenient for the user to find and review questions according to the knowledge points, thereby improving learning efficiency.
[0053] In addition to generating target keywords for each question, you can also extract the answer features of the question based on the answer content or answer analysis content of the question and insert them after the target keyword. Among them, the answer feature can be the solution method of the question or the principle or formula used to solve the question. For example, if the keyword of the question is "geometry", the answer feature of the question is extracted from its answer content or answer analysis. For example, if the answer skill is extracted as "add auxiliary line method", this answer skill can be inserted after the keyword as the answer feature, so that users can not only obtain the knowledge points involved in the question, but also obtain the answer skills of the question.
[0054] based on Figure 2In the illustrated embodiment, in a preferred implementation manner, after obtaining the index information of the question data set, the computer device may, in response to the user's editing operation on the index of the target question in the index information of the question data set, mark or write the error type of the incorrect answer to the target question in the index of the target question.
[0055] For example, the error type may be unclear basic concepts, incorrect question reading, calculation error, wrong thinking, or other error types.
[0056] The error type marked or written in the index of the target topic may be described and represented by text or by a graphic mark, which is not limited here.
[0057] like Figure 3 As shown in the index information of the created topic data set, the page number column indicates the page number of the topic in the topic data set, such as P1 indicates the first page of the topic in the topic data set. Figure 3 In the index information of the question data set shown, the user can view the index of each question and determine the location of the question based on the index. For example, for the question corresponding to the first row of indexes, the user can obtain that the question comes from P1:4 in a certain homework book "High 1 Optimization... Homework", that is, the question number 4 on page 1; the difficulty is II, that is, the difficulty level is II (basic problem-solving skills). In addition, the user can view the keywords corresponding to each question in the index information. The keywords represent the knowledge points corresponding to the question. Therefore, when the user needs to find the question corresponding to a certain knowledge point, the user can directly view the keywords in the index to find the question corresponding to the knowledge point, and quickly find the question based on the source information corresponding to the question in the index, which improves the efficiency of obtaining questions, saves user time, and improves learning efficiency.
[0058] In addition, the index information also records the difficulty of each question. Users can also choose questions that match their learning situation for review and revisit, which facilitates users to review in a targeted manner and improve learning efficiency.
[0059] In addition, the index information also records the error type identifier, using "○▽△□◇" to represent various error types such as unclear basic concepts, incorrect question reading, calculation errors, incorrect thinking, and other errors. Users can make a mark on a certain identifier, such as checking or making other marks on a certain identifier in "○▽△□◇" to indicate the reason why the question was answered incorrectly, so that they can know the reasons why these questions were answered incorrectly when reviewing them later.
[0060] based on Figure 2In the embodiment shown, in a preferred implementation manner, an index of the question is created based on the source information and feature information of the question to obtain the index information of the question data set. Another method may be to create a feature aggregation index of the question based on the feature information and source information of the question for each question in the question data set to obtain the feature aggregation index information of the question data set. The feature aggregation index information is based on at least one feature in the feature information of the question as the primary key, and the source information of multiple questions that meet the primary key is aggregated with the non-primary key features in the feature information, thereby obtaining the feature aggregation index information.
[0061] For example, the keyword of the question can be used as the primary key, and the source information and feature information of multiple questions that match the keyword can be aggregated together, such as Figure 5 In the second row of the table shown, multiple questions are aggregated with the keyword "function" as the primary key, and the source information and other non-primary key features (such as difficulty level) of multiple questions containing the primary key are associated and aggregated, and the source information and non-primary key features are separated by " / ". For example, one of the questions can be expressed as P2 / III, indicating that the keyword of the question is "function", which comes from the second question in the wrong question book, and the difficulty level is III. Similarly, the source information and non-primary key features of other questions can be aggregated based on the primary key, so that users can obtain multiple questions corresponding to the same keyword and the source information and difficulty levels of these multiple questions at a glance.
[0062] Furthermore, after obtaining the feature aggregation index information of the question data set, the feature aggregation index information of the question data set can be output, so that the user can determine the questions in the question data set according to the feature aggregation index information. The user can find the question corresponding to the knowledge point by directly viewing the keywords in the feature aggregation index information, and quickly find the question according to the source information corresponding to the question in the feature aggregation index information, and quickly obtain the difficulty level of the question, which improves the efficiency of obtaining questions, saves user time, and improves learning efficiency.
[0063] based on Figure 2 In the illustrated embodiment, in a preferred implementation manner, the computer device can also obtain a newly added topic in the topic data set, as well as the source information and feature information of the newly added topic, create an index of the newly added topic based on the source information and feature information of the newly added topic, and add the index of the newly added topic to the index information of the topic data set.
[0064] like Figure 4 As shown, the computer device can Figure 3The index information shown is printed as a paper file and output, and when there is a new topic in the topic data set, the source information and feature information of the new topic are obtained, and the index of the new topic is created according to the source information and feature information of the new topic, the index of the new topic is added to the index information of the topic data set, and the index of the new topic is output, that is, Figure 4 The page numbers shown are the indexes corresponding to Q1 and Q2.
[0065] Therefore, by adding index information to the newly added topic, the index information of the topic data set can be automatically updated in real time according to the user's learning progress, so that the content of the index information can match the user's real-time learning situation.
[0066] The computer device in the embodiment of the present application is described below. Figure 6 In the embodiment of the present application, one embodiment of the computer device includes:
[0067] The computer device 600 may include one or more central processing units (CPU) 601 and a memory 605 , wherein the memory 605 stores one or more application programs or data.
[0068] The memory 605 may be a volatile storage or a persistent storage. The program stored in the memory 605 may include one or more modules, each of which may include a series of instruction operations in the computer device. Furthermore, the central processing unit 601 may be configured to communicate with the memory 605 and execute a series of instruction operations in the memory 605 on the computer device 600.
[0069] The computer device 600 may also include one or more power supplies 602, one or more wired or wireless network interfaces 603, one or more input and output interfaces 604, and / or one or more operating systems, such as Windows Server™, Mac OS X™, Unix™, Linux™, FreeBSD™, etc.
[0070] The CPU 601 can execute the aforementioned Figure 2 The operations performed by the computer device in the illustrated embodiment will not be described in detail here.
[0071] The present application also provides a computer storage medium, wherein one embodiment includes: the computer storage medium stores instructions, and when the instructions are executed on a computer, the computer executes the aforementioned Figure 2 The operations performed by the computer device in the illustrated embodiment.
[0072] The present application also provides a computer program product, wherein one embodiment includes: when the computer program product is run on a computer device, the computer device executes the aforementioned Figure 2 The operations performed by the computer device in the illustrated embodiment.
[0073] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.
[0074] In the several embodiments provided in the present application, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be an indirect coupling or communication connection through some interfaces, devices or units, which can be electrical, mechanical or other forms.
[0075] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
[0076] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.
[0077] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application is essentially or the part that contributes to the prior art or all or part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including a number of instructions to enable a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, read-only memory), random access memory (RAM, random access memory), disk or optical disk and other media that can store program code.
Claims
1. A topic index creation method, characterized in that: The method is applied to a computer device; the method comprises: Acquire a question data set, where the question data set includes a plurality of questions; Acquire attribute information and feature information of each question in the question data set, wherein the attribute information includes source information of the question; For each question in the question data set, creating an index of the question according to the source information and the feature information of the question, and obtaining index information of the question data set; The index information of the topic data set is outputted, so that the user can determine the topic in the topic data set according to the index information of the topic data set.
2. The method according to claim 1, characterized in that The obtaining of attribute information of each question in the question data set includes: For each question in the question data set, information of a carrier for recording the question is obtained, as well as a position of the question in the carrier and / or identification information of the question.
3. The method according to claim 1, characterized in that Obtain feature information of each question in the question dataset, including: For each question in the question data set, obtaining a first difficulty score of a question that is the same as or similar to the question, and / or determining a second difficulty score according to a position of the question in a carrier for recording the question, and / or determining a third difficulty score according to a question knowledge type of the question, and / or determining a fourth difficulty score according to a character type of an answer to the question and the number of different character types; The difficulty score of the question is determined according to one or more of the first difficulty score, the second difficulty score, the third difficulty score, and the fourth difficulty score.
4. The method according to claim 1, characterized in that: Determining the difficulty score of the question according to one or more of the first difficulty score, the second difficulty score, the third difficulty score, and the fourth difficulty score includes: A weighted sum is taken for at least two of the first difficulty score, the second difficulty score, the third difficulty score, and the fourth difficulty score, and the obtained sum is used as the difficulty score of the question.
5. The method according to claim 1, characterized in that Obtain feature information of each question in the question dataset, including: Acquire a knowledge point word segmentation library, wherein the knowledge point word segmentation library includes a word segmentation corresponding to each knowledge point; Determine the keywords of each knowledge point in the word segmentation, and obtain keywords of multiple knowledge points; For each question in the question data set, the question stem is segmented to obtain multiple segmentations of the question stem, and a target keyword matching the segmentation of the question stem is determined from the keywords of the multiple knowledge points.
6. The method according to claim 1, characterized in that After obtaining the index information of the topic data set, the method further includes: In response to a user's editing operation on the index of the target topic in the index information of the topic data set, an error type of an incorrect answer to the target topic is written into the index of the target topic.
7. The method according to claim 1, characterized in that The step of creating an index of the topic according to the source information and the feature information of the topic to obtain index information of the topic data set includes: For each question in the question data set, a feature aggregation index of the question is created according to the feature information and the source information of the question, and feature aggregation index information of the question data set is obtained; the feature aggregation index information uses at least one feature in the feature information as a primary key, and aggregates the source information of multiple questions that meet the primary key with non-primary key features in the feature information; Outputting the index information of the topic data set so that the user can determine the topics in the topic data set according to the index information of the topic data set includes: Output feature aggregation index information of the topic data set so that the user can determine the topics in the topic data set according to the feature aggregation index information.
8. The method according to any one of claims 1 to 7, characterized in that: The method further comprises: Acquire a newly added topic in the topic data set, and acquire the source information and the feature information of the newly added topic; An index of the newly added topic is created according to the source information and the characteristic information of the newly added topic, and the index of the newly added topic is added to the index information of the topic data set.
9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the method according to any one of claims 1 to 8 is implemented.
10. A computer storage medium, characterized in that: The computer storage medium stores instructions, and when the instructions are executed on a computer, the computer is caused to perform the method according to any one of claims 1 to 8.