Knowledge question and answer rapid processing system based on artificial intelligence
Through the artificial intelligence-based knowledge question and answer system, using data acquisition, processing and matching modules, combined with user satisfaction evaluation, the flexibility and self-optimization problems of the existing system are solved, and more efficient knowledge question and answer processing is achieved.
Patent Information
- Application Number
- CN202510751234.8
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-06-06
- Publication Date
- 2025-09-30
- Estimated Expiration
- Not applicable · inactive patent
AI Technical Summary
Existing knowledge question-and-answer systems lack flexibility and depth, making it difficult to provide professional and detailed answers. They are also unable to optimize and update themselves, reducing work efficiency.
An artificial intelligence-based knowledge question and answer rapid processing system is adopted, including data acquisition, processing, matching and response modules. User question and answer data is processed through keyword extraction and speech recognition technology, and matched with the preset knowledge base, and adaptive learning optimization is performed in combination with user satisfaction evaluation.
It improves the flexibility and professionalism of the knowledge question-answering system, enhances the system's self-optimization capabilities, and improves work efficiency.
Smart Images
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Abstract
Description
Technical Field
[0001] The present application relates to the field of processing and analysis technology, and in particular to a knowledge question and answer rapid processing system based on artificial intelligence. Background Art
[0002] In the current information age, knowledge question-answering systems have become an important tool for acquiring information and knowledge. These systems typically analyze user questions and retrieve answers from large amounts of data. In recent years, with the development of artificial intelligence (AI), particularly in the fields of natural language processing and artificial intelligence, knowledge question-answering systems have become more capable of understanding and processing natural language at a deeper level.
[0003] In the related technologies, the existing knowledge question and answer processing systems lack sufficient flexibility and depth, making it difficult to provide professional and detailed answers, and are unable to improve and optimize the knowledge question and answer processing systems based on user feedback, making it difficult for the knowledge question and answer processing systems to self-optimize and update during long-term use, reducing the working efficiency of the knowledge question and answer processing systems, and there is room for improvement. Summary of the Invention
[0004] In response to the deficiencies of the existing technology, this application provides a knowledge question and answer rapid processing system based on artificial intelligence.
[0005] In the first aspect, the present application provides a knowledge question-answering rapid processing system based on artificial intelligence, which adopts the following technical solutions:
[0006] A knowledge question-answering rapid processing system based on artificial intelligence, comprising:
[0007] The data acquisition module is used to collect and respond to the knowledge question and answer instructions output by the target user, and obtain the knowledge question and answer method and knowledge question and answer data corresponding to the target user;
[0008] The data processing module is used to analyze and process the knowledge question and answer method and knowledge question and answer data corresponding to the target user, and identify the question and answer keyword data corresponding to the target user based on the analysis and processing results, and mark the question and answer keyword data corresponding to the target user as the question and answer data to be answered;
[0009] The data matching module is used to compare the target user's corresponding question-answering data with the question data in the preset knowledge base, and analyze the matching degree between the target user's corresponding question-answering data and the question data in the preset knowledge base;
[0010] The question-answer response module is used to confirm the answer content based on the matching degree between the question-answer data corresponding to the target user and the question data in the preset knowledge base, and set the answer content as the target answer content corresponding to the target user.
[0011] Preferably, the knowledge question and answer method corresponding to the target user includes a text question and answer method and a voice question and answer method. The text question and answer method is set for the target user to input text for question and answer, and the voice question and answer method is set for the target user to input voice for question and answer.
[0012] Preferably, the knowledge question and answer method and knowledge question and answer data corresponding to the target user are analyzed and processed, specifically including:
[0013] If the target user's corresponding knowledge question and answer mode is a text question and answer mode, the question and answer text data input by the target user is obtained, and the corresponding keywords in the question and answer text data input by the target user are identified through keyword extraction technology, and the keywords are marked as the question and answer keyword data corresponding to the target user, and the question and answer keyword data corresponding to the target user is stored in the question and answer keyword data set;
[0014] If the knowledge question and answer method corresponding to the target user is a voice question and answer method, the question and answer voice data input by the target user is obtained, and the question and answer text data corresponding to the question and answer voice data input by the target user is extracted through voice recognition technology, and the question and answer text data is processed according to the processing method of the text question and answer method, and then the question and answer keyword data corresponding to the target user is confirmed, and the question and answer keyword data corresponding to the target user is stored in the question and answer keyword data set.
[0015] Preferably, the data matching module includes a classification unit, a retrieval unit and a matching unit;
[0016] The classification unit is used to determine the question and answer type corresponding to the question and answer data, and determine the knowledge base type corresponding to the question and answer type based on the question and answer type, and further determine the question data corresponding to the preset knowledge base based on the knowledge base type;
[0017] The retrieval unit is used to retrieve information related to the question-answering data from the question data corresponding to the preset knowledge base;
[0018] The matching unit is used to analyze and process the retrieved relevant information, and then confirm the matching degree between the question-answering data corresponding to the target user and the question data in the preset knowledge base.
[0019] Preferably, processing the preset knowledge base specifically includes:
[0020] The corresponding question data in the preset knowledge base is obtained, and the preset knowledge base is divided into sub-knowledge bases based on a preset division method, and then the knowledge base type corresponding to each sub-knowledge base and the keyword data corresponding to each question data in each sub-knowledge base are confirmed.
[0021] Preferably, determining the matching degree between the question-answering data corresponding to the target user and the question data in the preset knowledge base specifically includes:
[0022] Obtain the question and answer keyword data corresponding to the target user, and then confirm the question and answer keyword dataset G corresponding to the target user;
[0023] Confirm the keyword data G corresponding to each question data in each sub-knowledge base i , where i represents the number corresponding to each question data, i=1,2,3......j;
[0024] By formula Confirm the matching degree αi between the target user's corresponding question and answer data and each question data in the preset knowledge base;
[0025] The matching degrees αi of the question-answering data corresponding to the target user and the question data in the preset knowledge base are sorted from large to small, and the top-ranked question data are extracted according to the sorting order, and the top-ranked question data are set as the target matching question data, and the answer content corresponding to the target matching question data is set as the target answer content corresponding to the target user.
[0026] Preferably, it also includes:
[0027] If the knowledge question and answer mode corresponding to the target user is a voice question and answer mode, the target answer content corresponding to the target user is converted into voice through a preset device and output.
[0028] Preferably, it also includes a user satisfaction evaluation module and an adaptive learning module;
[0029] The user satisfaction evaluation module includes a satisfaction information collection unit and a satisfaction evaluation coefficient confirmation unit. The satisfaction information collection unit is used to collect target user satisfaction data on target answer content. The satisfaction data includes direct rating values and indirect behavior data. The indirect behavior data includes viewing time and click-through rate corresponding to the target answer content.
[0030] The satisfaction evaluation coefficient confirmation unit is used to confirm the satisfaction evaluation coefficient corresponding to the target user based on the target user's satisfaction data on the target answer content;
[0031] The process of determining the satisfaction evaluation coefficient corresponding to the target user includes:
[0032] By formula Determine the target user's satisfaction evaluation coefficient β, where w and w' represent the target user's direct score and direct score threshold for the target answer content, respectively; c and c' represent the target user's viewing time and reference viewing time threshold for the target answer content, respectively; p and p' represent the target user's click-through rate and reference click-through rate threshold for the target answer content, respectively; and e is a natural constant.
[0033] The adaptive learning module is used to optimize the target answer content according to the satisfaction evaluation coefficient corresponding to the target user;
[0034] The process of optimizing the target answer content based on the target user's corresponding satisfaction evaluation coefficient specifically includes:
[0035] Confirm the preset time window, and collect the real-time satisfaction evaluation coefficient corresponding to the target user within the preset time window to form a time series, and express the real-time satisfaction evaluation coefficient according to the time series using the function β(t);
[0036] By formula Determine the target user's satisfaction change coefficient k for the target answer content, where β′ represents the preset standard satisfaction evaluation threshold;
[0037] Compare the target user's satisfaction change coefficient k for the target answer content with the preset satisfaction change threshold k';
[0038] If the target user's satisfaction coefficient for the target answer content is k ≥ k′, then there is no need to optimize the target answer content;
[0039] If the target user's satisfaction coefficient with the target answer content is less than k', the target answer content needs to be optimized.
[0040] Secondly, this application provides a method for rapid processing of knowledge questions and answers based on artificial intelligence, which adopts the following technical solutions:
[0041] A method for rapidly processing knowledge questions and answers based on artificial intelligence, comprising the following steps:
[0042] Collect and respond to the knowledge question and answer instructions output by the target user, and obtain the target user's corresponding knowledge question and answer method and knowledge question and answer data;
[0043] Analyze and process the knowledge question and answer method and knowledge question and answer data corresponding to the target user, and identify the question and answer keyword data corresponding to the target user based on the analysis and processing results, and mark the question and answer keyword data corresponding to the target user as waiting question and answer data;
[0044] Compare the target user's corresponding question-answering data with the question data in the preset knowledge base, and analyze the matching degree between the target user's corresponding question-answering data and the question data in the preset knowledge base;
[0045] The answer content is determined based on the matching degree between the question-answering data corresponding to the target user and the question data in the preset knowledge base, and the answer content is set as the target answer content corresponding to the target user.
[0046] In a third aspect, the present application provides a computer-readable storage medium storing instructions. When the instructions are executed on a computer, the computer executes any one of the above-mentioned artificial intelligence-based knowledge question and answer rapid processing systems.
[0047] In summary, this application includes at least one of the following beneficial technical effects:
[0048] 1. The present invention provides an artificial intelligence-based knowledge question and answer rapid processing system, which collects and analyzes the knowledge question and answer methods and knowledge question and answer data corresponding to the target user, identifies the target user's corresponding data for question and answer, and compares the target user's corresponding data for question and answer with the question data in the preset knowledge base, thereby identifying the matching degree between the target user's corresponding data for question and answer and the question data in the preset knowledge base, and then identifies the target answer content corresponding to the target user based on the matching degree between the target user's corresponding data for question and answer and the question data in the preset knowledge base, thereby effectively improving the flexibility and professionalism of the knowledge question and answer processing system, thereby effectively improving the working efficiency of the knowledge question and answer processing system;
[0049] 2. By collecting the target user's satisfaction data on the target answer content, the corresponding satisfaction evaluation coefficient of the target user is confirmed, and then the knowledge question and answer processing system is optimized and updated based on the corresponding satisfaction evaluation coefficient of the target user, thereby effectively improving the work efficiency of the knowledge question and answer processing system. BRIEF DESCRIPTION OF THE DRAWINGS
[0050] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for describing the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.
[0051] Figure 1 This is a system diagram of the rapid processing of knowledge questions and answers based on artificial intelligence in an embodiment of the present application.
[0052] Figure 2 This is a flow chart of a method for rapid processing of knowledge questions and answers based on artificial intelligence in an embodiment of the present application. DETAILED DESCRIPTION
[0053] The following is combined with Figure 1-2 This application is described in further detail.
[0054] Example 1
[0055] The embodiment of the present application discloses a knowledge question and answer rapid processing system based on artificial intelligence.
[0056] Reference Figure 1 , a knowledge question answering fast processing system based on artificial intelligence, including:
[0057] The data acquisition module is used to collect and respond to the knowledge question and answer instructions output by the target user, and obtain the knowledge question and answer method and knowledge question and answer data corresponding to the target user;
[0058] The data processing module is used to analyze and process the knowledge question and answer method and knowledge question and answer data corresponding to the target user, and identify the question and answer keyword data corresponding to the target user based on the analysis and processing results, and mark the question and answer keyword data corresponding to the target user as the question and answer data to be answered;
[0059] The data matching module is used to compare the target user's corresponding question-answering data with the question data in the preset knowledge base, and analyze the matching degree between the target user's corresponding question-answering data and the question data in the preset knowledge base;
[0060] The question-answer response module is used to confirm the answer content based on the matching degree between the question-answer data corresponding to the target user and the question data in the preset knowledge base, and set the answer content as the target answer content corresponding to the target user.
[0061] Through the above technical solution, by collecting and analyzing the knowledge question and answer methods and knowledge question and answer data corresponding to the target user, the data to be asked and answered corresponding to the target user is confirmed, and the data to be asked and answered corresponding to the target user is compared with the question data in the preset knowledge base, and then the matching degree between the data to be asked and answered corresponding to the target user and the question data in the preset knowledge base is confirmed, and then the target answer content corresponding to the target user is confirmed based on the matching degree between the data to be asked and answered corresponding to the target user and the question data in the preset knowledge base, thereby effectively improving the flexibility and professionalism of the knowledge question and answer processing system, thereby effectively improving the work efficiency of the knowledge question and answer processing system.
[0062] Furthermore, the knowledge question and answer method corresponding to the target user includes a text question and answer method and a voice question and answer method. The text question and answer method is set for the target user to input text for question and answer, and the voice question and answer method is set for the target user to input voice for question and answer.
[0063] It should be noted that the analysis and processing of the knowledge question and answer methods and knowledge question and answer data corresponding to the target users specifically includes:
[0064] If the target user's corresponding knowledge question and answer mode is a text question and answer mode, the question and answer text data input by the target user is obtained, and the corresponding keywords in the question and answer text data input by the target user are identified through keyword extraction technology, and the keywords are marked as the question and answer keyword data corresponding to the target user, and the question and answer keyword data corresponding to the target user is stored in the question and answer keyword data set;
[0065] If the knowledge question and answer method corresponding to the target user is a voice question and answer method, the question and answer voice data input by the target user is obtained, and the question and answer text data corresponding to the question and answer voice data input by the target user is extracted through voice recognition technology, and the question and answer text data is processed according to the processing method of the text question and answer method, and then the question and answer keyword data corresponding to the target user is confirmed, and the question and answer keyword data corresponding to the target user is stored in the question and answer keyword data set.
[0066] For example, if the question and answer text data input by the target user is "Who is the protagonist of the Three Visits to the Thatched Cottage in Romance of the Three Kingdoms", then Romance of the Three Kingdoms, Three Visits to the Thatched Cottage and protagonist are set as question and answer keyword data, and Romance of the Three Kingdoms, Three Visits to the Thatched Cottage and protagonist are stored in the question and answer keyword data set.
[0067] Furthermore, the data matching module includes a classification unit, a retrieval unit and a matching unit;
[0068] The classification unit is used to determine the question and answer type corresponding to the question and answer data, and determine the knowledge base type corresponding to the question and answer type based on the question and answer type, and further determine the question data corresponding to the preset knowledge base based on the knowledge base type;
[0069] The retrieval unit is used to retrieve information related to the question-answering data from the question data corresponding to the preset knowledge base;
[0070] The matching unit is used to analyze and process the retrieved relevant information, and then confirm the matching degree between the question-answering data corresponding to the target user and the question data in the preset knowledge base.
[0071] Specifically, in the embodiment of the present application, the question and answer data is consistent with the question and answer text data input by the target user. For example, the question and answer data is "Who is the protagonist of the three visits to the thatched cottage in the Romance of the Three Kingdoms?", then the question and answer type corresponding to the question and answer data is the Romance of the Three Kingdoms, and the knowledge base type corresponding to the question and answer type is the knowledge base corresponding to the Romance of the Three Kingdoms, and then the question data about the Romance of the Three Kingdoms is confirmed from the knowledge base corresponding to the Romance of the Three Kingdoms.
[0072] It should be noted that processing the preset knowledge base specifically includes:
[0073] The corresponding question data in the preset knowledge base is obtained, and the preset knowledge base is divided into sub-knowledge bases based on a preset division method, and then the knowledge base type corresponding to each sub-knowledge base and the keyword data corresponding to each question data in each sub-knowledge base are confirmed.
[0074] For example, if the preset knowledge base includes four great classics, the preset knowledge base is divided into Romance of the Three Kingdoms sub-knowledge base type, Dream of the Red Chamber sub-knowledge base type, Water Margin sub-knowledge base type, and Journey to the West sub-knowledge base type.
[0075] It should be noted that determining the matching degree between the target user's corresponding question-answering data and the question data in the preset knowledge base specifically includes:
[0076] Obtain the question and answer keyword data corresponding to the target user, and then confirm the question and answer keyword dataset G corresponding to the target user;
[0077] Confirm the keyword data G corresponding to each question data in each sub-knowledge base i , where i represents the number corresponding to each question data, i=1,2,3......j;
[0078] By formula Confirm the matching degree αi between the target user's corresponding question and answer data and each question data in the preset knowledge base;
[0079] The matching degree α between the target user's corresponding question and answer data and the question data in the preset knowledge base i Sort by large to small, extract the top-ranked question data according to the sorting order, set the top-ranked question data as the target matching question data, and set the answer content corresponding to the target matching question data as the target answer content corresponding to the target user.
[0080] For example, if the question data in the preset knowledge base include the first question data, the second question data and the third question data, the first question data is "Who is the person who Liu Bei invited to come out of seclusion after visiting the thatched cottage three times in the Romance of the Three Kingdoms", the second question data is "What characters are included in the Romance of the Three Kingdoms" and the third question data is "Who is the protagonist of the Three Kingdoms who visited the thatched cottage three times in the Romance of the Three Kingdoms", then the keyword data of the first question data are The Romance of the Three Kingdoms, Three Visits to the Thatched Cottage, Liu Bei, Please, Come Out of Seclusion, the keyword data of the second question data are The Romance of the Three Kingdoms, Characters, and the keyword data of the third question data are The Romance of the Three Kingdoms, In the three visits to the thatched cottage and the protagonist, the keyword data in the first question data, the second question data and the third question data are compared and calculated with the question and answer keyword data respectively, and it is confirmed that the keyword data in the third question data has the greatest matching degree with the question and answer keyword data, then the answer content corresponding to the third question data is set as the target answer content corresponding to the target user. In this embodiment of the application, the top-ranked question data can also be set as the target matching question data, which can increase the knowledge horizon of the target user and then expand the relevant knowledge, thereby improving work efficiency.
[0081] Furthermore, it also includes:
[0082] If the knowledge question and answer mode corresponding to the target user is a voice question and answer mode, the target answer content corresponding to the target user is converted into voice through a preset device and output.
[0083] It should be noted that it also includes a user satisfaction evaluation module and an adaptive learning module;
[0084] The user satisfaction evaluation module includes a satisfaction information collection unit and a satisfaction evaluation coefficient confirmation unit. The satisfaction information collection unit is used to collect target user satisfaction data on target answer content. The satisfaction data includes direct rating values and indirect behavior data. The indirect behavior data includes viewing time and click-through rate corresponding to the target answer content.
[0085] The satisfaction evaluation coefficient confirmation unit is used to confirm the satisfaction evaluation coefficient corresponding to the target user based on the target user's satisfaction data on the target answer content;
[0086] The process of determining the satisfaction evaluation coefficient corresponding to the target user includes:
[0087] By formula Determine the target user's satisfaction evaluation coefficient β, where w and w' represent the target user's direct score and direct score threshold for the target answer content, respectively; c and c' represent the target user's viewing time and reference viewing time threshold for the target answer content, respectively; p and p' represent the target user's click-through rate and reference click-through rate threshold for the target answer content, respectively; and e is a natural constant.
[0088] The adaptive learning module is used to optimize the target answer content according to the satisfaction evaluation coefficient corresponding to the target user;
[0089] The process of optimizing the target answer content based on the target user's corresponding satisfaction evaluation coefficient specifically includes:
[0090] Confirm the preset time window, and collect the real-time satisfaction evaluation coefficient corresponding to the target user within the preset time window to form a time series, and express the real-time satisfaction evaluation coefficient according to the time series using the function β(t);
[0091] By formula Determine the target user's satisfaction change coefficient k for the target answer content, where β′ represents the preset standard satisfaction evaluation threshold;
[0092] Compare the target user's satisfaction change coefficient k for the target answer content with the preset satisfaction change threshold k';
[0093] If the target user's satisfaction coefficient for the target answer content is k ≥ k′, then there is no need to optimize the target answer content;
[0094] If the target user's satisfaction coefficient with the target answer content is less than k', the target answer content needs to be optimized.
[0095] Through the above technical solution, by collecting the target user's satisfaction data on the target answer content, the target user's corresponding satisfaction evaluation coefficient is confirmed, and then the knowledge question and answer processing system is optimized and updated based on the target user's corresponding satisfaction evaluation coefficient, thereby effectively improving the work efficiency of the knowledge question and answer processing system.
[0096] Example 2
[0097] The embodiment of the present application also discloses a method for quickly processing knowledge questions and answers based on artificial intelligence.
[0098] Reference Figure 2 , a fast processing method for knowledge question answering based on artificial intelligence, comprising the following steps:
[0099] Collect and respond to the knowledge question and answer instructions output by the target user, and obtain the target user's corresponding knowledge question and answer method and knowledge question and answer data;
[0100] Analyze and process the knowledge question and answer method and knowledge question and answer data corresponding to the target user, and identify the question and answer keyword data corresponding to the target user based on the analysis and processing results, and mark the question and answer keyword data corresponding to the target user as waiting question and answer data;
[0101] Compare the target user's corresponding question-answering data with the question data in the preset knowledge base, and analyze the matching degree between the target user's corresponding question-answering data and the question data in the preset knowledge base;
[0102] The answer content is determined based on the matching degree between the question-answering data corresponding to the target user and the question data in the preset knowledge base, and the answer content is set as the target answer content corresponding to the target user.
[0103] The above content is merely an example and explanation of the concept of the present invention. Those skilled in the art may make various modifications or additions to the described specific embodiments or replace them in a similar manner. As long as they do not deviate from the concept of the invention or exceed the scope defined by the claims, they should all fall within the scope of protection of the present invention.
[0104] Throughout this specification, references to terms such as "one embodiment," "example," or "specific example" indicate that the specific features, structures, materials, or characteristics described in conjunction with that embodiment or example are included in at least one embodiment or example of the present invention. In this specification, schematic representations of these terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.
[0105] The preferred embodiments of the present invention disclosed above are intended only to help illustrate the present invention. These preferred embodiments do not exhaustively describe all details, nor do they limit the present invention to specific embodiments. Obviously, many modifications and variations are possible based on the contents of this specification. These embodiments are selected and described in detail in this specification to better explain the principles and practical applications of the present invention, thereby enabling those skilled in the art to better understand and utilize the present invention. The present invention is limited only by the claims and their full scope and equivalents.
Claims
1. A knowledge question and answer fast processing system based on artificial intelligence, characterized by: include: The data acquisition module is used to collect and respond to the knowledge question and answer instructions output by the target user, and obtain the knowledge question and answer method and knowledge question and answer data corresponding to the target user; The data processing module is used to analyze and process the knowledge question and answer method and knowledge question and answer data corresponding to the target user, and identify the question and answer keyword data corresponding to the target user based on the analysis and processing results, and mark the question and answer keyword data corresponding to the target user as the question and answer data to be answered; The data matching module is used to compare the target user's corresponding question-answering data with the question data in the preset knowledge base, and analyze the matching degree between the target user's corresponding question-answering data and the question data in the preset knowledge base; The question-answer response module is used to confirm the answer content based on the matching degree between the question-answer data corresponding to the target user and the question data in the preset knowledge base, and set the answer content as the target answer content corresponding to the target user.
2. The artificial intelligence-based knowledge question-answering rapid processing system according to claim 1, characterized in that: The knowledge question and answer mode corresponding to the target user includes a text question and answer mode and a voice question and answer mode. The text question and answer mode is set to allow the target user to input text for question and answer, and the voice question and answer mode is set to allow the target user to input voice for question and answer.
3. The artificial intelligence-based knowledge question-answering rapid processing system according to claim 2, characterized in that: Analyze and process the knowledge question and answer methods and knowledge question and answer data corresponding to the target users, including: If the target user's corresponding knowledge question and answer mode is a text question and answer mode, the question and answer text data input by the target user is obtained, and the corresponding keywords in the question and answer text data input by the target user are identified through keyword extraction technology, and the keywords are marked as the question and answer keyword data corresponding to the target user, and the question and answer keyword data corresponding to the target user is stored in the question and answer keyword data set; If the knowledge question and answer method corresponding to the target user is a voice question and answer method, the question and answer voice data input by the target user is obtained, and the question and answer text data corresponding to the question and answer voice data input by the target user is extracted through voice recognition technology, and the question and answer text data is processed according to the processing method of the text question and answer method, and then the question and answer keyword data corresponding to the target user is confirmed, and the question and answer keyword data corresponding to the target user is stored in the question and answer keyword data set.
4. The artificial intelligence-based knowledge question-answering rapid processing system according to claim 1, characterized in that: The data matching module includes a classification unit, a retrieval unit and a matching unit; The classification unit is used to determine the question and answer type corresponding to the question and answer data, and determine the knowledge base type corresponding to the question and answer type based on the question and answer type, and further determine the question data corresponding to the preset knowledge base based on the knowledge base type; The retrieval unit is used to retrieve information related to the question-answering data from the question data corresponding to the preset knowledge base; The matching unit is used to analyze and process the retrieved relevant information, and then confirm the matching degree between the question-answering data corresponding to the target user and the question data in the preset knowledge base.
5. The artificial intelligence-based knowledge question-answering rapid processing system according to claim 4, characterized in that: Processing the preset knowledge base includes: The corresponding question data in the preset knowledge base is obtained, and the preset knowledge base is divided into sub-knowledge bases based on a preset division method, and then the knowledge base type corresponding to each sub-knowledge base and the keyword data corresponding to each question data in each sub-knowledge base are confirmed.
6. The artificial intelligence-based knowledge question-answering rapid processing system according to claim 5 is characterized in that , confirm the matching degree between the target user's corresponding question and answer data and the question data in the preset knowledge base, specifically including: Obtain the question and answer keyword data corresponding to the target user, and then confirm the question and answer keyword dataset G corresponding to the target user; Confirm the keyword data G corresponding to each question data in each sub-knowledge base i , where i represents the number corresponding to each question data, i=1,2,3......j; By formula Confirm the matching degree αi between the target user's corresponding question and answer data and each question data in the preset knowledge base; The matching degree α between the target user's corresponding question and answer data and the question data in the preset knowledge base i Sort by large to small, extract the top-ranked question data according to the sorting order, set the top-ranked question data as the target matching question data, and set the answer content corresponding to the target matching question data as the target answer content corresponding to the target user.
7. The artificial intelligence-based knowledge question-answering rapid processing system according to claim 6, characterized in that: Also includes: If the knowledge question and answer mode corresponding to the target user is a voice question and answer mode, the target answer content corresponding to the target user is converted into voice through a preset device and output.
8. The artificial intelligence-based knowledge question-answering rapid processing system according to claim 6, characterized in that: It also includes a user satisfaction assessment module and an adaptive learning module; The user satisfaction evaluation module includes a satisfaction information collection unit and a satisfaction evaluation coefficient confirmation unit. The satisfaction information collection unit is used to collect target user satisfaction data on target answer content. The satisfaction data includes direct rating values and indirect behavior data. The indirect behavior data includes viewing time and click-through rate corresponding to the target answer content. The satisfaction evaluation coefficient confirmation unit is used to confirm the satisfaction evaluation coefficient corresponding to the target user based on the target user's satisfaction data on the target answer content; The process of determining the satisfaction evaluation coefficient corresponding to the target user includes: By formula Determine the target user's satisfaction evaluation coefficient β, where w and w' represent the target user's direct score and direct score threshold for the target answer content, respectively; c and c' represent the target user's viewing time and reference viewing time threshold for the target answer content, respectively; p and p' represent the target user's click-through rate and reference click-through rate threshold for the target answer content, respectively; and e is a natural constant. The adaptive learning module is used to optimize the target answer content according to the satisfaction evaluation coefficient corresponding to the target user; The process of optimizing the target answer content based on the target user's corresponding satisfaction evaluation coefficient specifically includes: Confirm the preset time window, and collect the real-time satisfaction evaluation coefficient corresponding to the target user within the preset time window to form a time series, and express the real-time satisfaction evaluation coefficient according to the time series using the function β(t); By formula Determine the target user's satisfaction change coefficient k for the target answer content, where β′ represents the preset standard satisfaction evaluation threshold; Compare the target user's satisfaction change coefficient k for the target answer content with the preset satisfaction change threshold k'; If the target user's satisfaction coefficient for the target answer content is k ≥ k′, then there is no need to optimize the target answer content; If the target user's satisfaction coefficient with the target answer content is less than k', the target answer content needs to be optimized.
9. A method for rapidly processing knowledge questions and answers based on artificial intelligence, applied to a system for rapidly processing knowledge questions and answers based on artificial intelligence as claimed in claims 1 to 8, characterized in that: The following steps are involved: Collect and respond to the knowledge question and answer instructions output by the target user, and obtain the target user's corresponding knowledge question and answer method and knowledge question and answer data; Analyze and process the knowledge question and answer method and knowledge question and answer data corresponding to the target user, and identify the question and answer keyword data corresponding to the target user based on the analysis and processing results, and mark the question and answer keyword data corresponding to the target user as waiting question and answer data; Compare the target user's corresponding question-answering data with the question data in the preset knowledge base, and analyze the matching degree between the target user's corresponding question-answering data and the question data in the preset knowledge base; The answer content is determined based on the matching degree between the question-answering data corresponding to the target user and the question data in the preset knowledge base, and the answer content is set as the target answer content corresponding to the target user.
10. A computer-readable storage medium, characterized in that: Instructions are stored, and when the instructions are run on a computer, the computer is caused to execute the artificial intelligence-based knowledge question and answer rapid processing system as described in any one of claims 1 to 8.