185 results about "Contextual Associations" patented technology

The invention discloses a traffic engineering large model intelligent question answering system and method based on a knowledge graph, and the method comprises the steps: extracting a structured degree feature, a semantic ambiguity feature and a context association feature through receiving and analyzing a natural language query statement inputted by a user, generating a retrieval intention vector, and carrying out the retrieval of the retrieval intention vector; and dynamically selecting a retrieval path according to the intention classification model. And according to the retrieval path, constructing a structured query statement or a semantic vector, and respectively retrieving in the knowledge graph and the vector database to obtain a first retrieval result and a second retrieval result. Further performing bidirectional verification through entity consistency, semantic similarity and relation connectivity indexes, screening a candidate result set, and constructing a reasoning chain; if the inference chain is broken, a large model inference gap complementation mechanism is adopted to generate relay nodes, a complete inference chain is formed, and inference type answer output is generated based on the complete chain. According to the method, the retrieval accuracy and reasoning continuity of the question-answering system are improved.

The invention discloses a search method and system based on computer natural language processing, and the method comprises the steps: extracting a synonym set and a context association relationship of keywords in a query text through a preset semantic knowledge graph, and generating a semantic vector representing a semantic dimension in combination with a deep learning model; extracting a historical behavior feature sequence from the query log based on the semantic vector, and analyzing a user search intention by adopting an attention mechanism model; performing similarity matching on the pre-constructed database by utilizing a semantic matching algorithm, and screening an information matching set meeting a threshold value; performing distributed processing on the matching set through a context-aware dynamic fragmentation algorithm, and constructing an index fragmentation cluster; semantic aggregation is realized by adopting a cross-fragment graph attention network, and an optimized search result set is generated through dynamic semantic projection. According to the method, through multi-modal fusion of the semantic knowledge graph and deep learning and in combination with a dynamic distributed processing architecture, the accuracy of search intention recognition and the efficiency of large-scale semantic matching are improved.

The invention provides a multi-round dialogue intention recognition method and system based on self-adaptive semantic understanding, and relates to the technical field of artificial intelligence, and the method comprises the steps: obtaining a natural language dialogue text of a current round of a user, and taking the natural language dialogue text as original input data; based on original input data, multi-level semantic features are extracted through a dynamic semantic coding algorithm, and semantic vector representation of a current round of dialogue is generated; setting three fixed anchor points in a semantic vector space based on a current round semantic vector and a historical dialogue state vector to form a triangular analysis structure; performing gridding segmentation on the triangular analysis structure, and generating a feature adjustment value according to distribution characteristics of segmented grids; and dynamically correcting the extraction process of the context-related features by using the feature adjustment value to obtain the corrected context-related features. According to the method, end-to-end optimization is realized in multiple rounds of interaction scenes such as customer service and intelligent assistants through full-process design.

The invention relates to the technical field of intelligent customer service control, and discloses a federated learning driven customer service robot cooperative control method and system. The method comprises the following steps: deploying a local intention recognition model at a plurality of nodes, collecting a user dialogue stream, extracting a semantic behavior track fragment, and generating a behavior feature vector set containing a time sequence and context association; the federal cooperative controller performs periodic aggregation, constructs a cross-node feature alignment mapping table based on trajectory similarity, and generates a global behavior feature distribution map; calculating node feature offset, screening high-contribution-degree nodes in combination with a sparse activation threshold, and allocating aggregation tasks; a knowledge distillation compression model is used at the high-contribution-degree nodes, weight updating parameters are extracted, compensation coefficients are added, and an encrypted updating package is generated; and the federal cooperative controller carries out heterogeneous fusion on the encrypted packet, reconstructs a global intention decision tree and carries out segmentation and distribution, so that efficient cooperation and optimization are realized, and privacy protection and service adaptability are considered.

The invention discloses an AI-based conference record data searching method and system, and the method comprises the steps: carrying out the feature extraction and alignment through a multi-mode fusion neural network according to the voice, text, image and video data collected in a conference process, and obtaining a semantic representation vector; according to the semantic representation vector, combining context information of the conference scene, and utilizing a pre-trained context perception model to perform semantic enhancement processing to generate an enhanced semantic vector with context association; according to the enhanced semantic vector, combining with an external knowledge base, and utilizing a dynamic knowledge graph construction algorithm to generate a knowledge graph related to the conference theme in real time; and according to the knowledge graph, intelligent retrieval and recommendation of conference record data are carried out by using a graph neural network. By utilizing the embodiment of the invention, the intelligent retrieval efficiency and accuracy of the conference record can be improved.

The invention provides a disease science popularization error correction method and system based on artificial intelligence. According to the method, the disease knowledge query content input by the user and the associated science popularization text data source are obtained, the semantic focus is dynamically recognized by utilizing the pre-trained language model, and the context-associated semantic focus distribution graph is generated. On the basis of cross-modal correlation analysis of a map and multi-source heterogeneous medical representation, a potential cognitive deviation region is positioned, and an error correction priority queue is constructed in combination with authority weight differences of medical knowledge nodes. Incremental correction is further carried out on the error expression according to the queue priority, and an error correction feedback result containing medical traceability information is generated while the original semantic intention of the user is reserved; according to the technical scheme provided by the invention, high-precision error correction of disease science popularization content and closed-loop feedback of medical traceability information are realized, and user cognition accuracy and knowledge credibility are improved.

The invention discloses a dynamic sensitive information filtering system and method based on context semantic understanding, and relates to the technical field of information security and natural language processing. Comprising the steps of 1, creating a dynamic sensitive information filtering system, 2, carrying out cleaning, structuring and standardization processing on an input text through a text preprocessing module, 3, capturing deep semantic features of preprocessed text data through a semantic feature extraction module by utilizing a deep learning model, constructing a context-associated semantic representation space, and carrying out dynamic sensitive information filtering on the context-associated semantic representation space. 4, performing multi-level sensitive information detection based on the semantic features through a sensitive information identification module, and identifying the type, the position and the risk level of the sensitive content; 5, on-line iteration of knowledge base and model ability is carried out through a dynamic updating module to cope with dynamic changes of sensitive information types, and 6, safety disposal is carried out on detected sensitive information through a result output module, a filtering result is output, auditing tracing ability is provided, and the auditing tracing ability is provided. And 7, forming a system optimization closed loop through a feedback mechanism module according to user feedback and manual auditing, wherein the system optimization closed loop is used for continuously improving the detection accuracy and adaptability.

The invention relates to the technical field of multi-modal large language models, in particular to a government affair service content navigation method and system based on a large language model, and the method comprises the following steps: receiving multi-modal information input by a user through texts, voices or pictures; the voice is converted into a text, and character information in the picture is analyzed by using an OCR (Optical Character Recognition) technology; fusing multi-modal data, and inputting the fused multi-modal data into a large language model for semantic understanding and context association analysis; matching items are retrieved in combination with a local government affair knowledge base, and an initial recommendation list is generated; the recommendation result is displayed through the intelligent assistant, and interaction optimization options are provided; the method has the beneficial effects that the current government affair service content navigation mode is optimized through the multi-modal capability, the retrieval enhancement capability and the content generation capability of the large language model, so that the navigation is more modal and more intelligent, and meanwhile, the privacy and authority of data reply are ensured.

The invention relates to the technical field of intelligent customer service, and provides an AI intelligent customer service system based on a large model, and the system is characterized in that an unstructured text processed by a multi-source knowledge fusion subsystem is reconstructed into structured knowledge entries with a multi-dimensional label system, intention classification and context association, and the structured knowledge entries are dynamically integrated into an enterprise knowledge graph; the response generation subsystem is used for receiving the content queried by the user, analyzing the intention through a deep semantic understanding model in combination with an enterprise knowledge graph, and dynamically maintaining the context in combination with a multi-round dialogue state tracking technology; meanwhile, emotion dimension analysis is carried out on the content, and a personalized response is generated by fusing user service feedback; and the autonomous optimization subsystem dynamically adjusts the weight distribution of structured knowledge entries in the enterprise knowledge graph and the priority of process nodes in combination with an attribution analysis result, and triggers a knowledge updating and API process reconstruction mechanism. The method has self-learning and continuous optimization capabilities, and continuously improves the service quality and the user experience.

The invention relates to a text semantic conflict detection method for constructing context association based on a large model. The method comprises the following steps: S1, segmenting an original text by using a segmentation function; s2, performing triple information extraction on each segmented text by using a large model to generate a data set of an entity corresponding relationship; s3, extracting data groups of all entity pairs and relationships, and performing confidence evaluation; s4, aggregating all triads, constructing a affair graph, and pointing out a contradictory relationship with relatively low confidence in the affair graph; and S5, for the found contradictory relationship, performing semantic conflict repair in the text in combination with the large model. According to the method, the accuracy of information generation of the large language model is remarkably improved in a context association mode.

The invention relates to the technical field of artificial intelligence natural language processing, and provides an intelligent multi-round question-answering system based on a large model, comprising a semantic understanding module for generating a semantic understanding state value by extracting a semantic feature vector input by a user and analyzing context association; the context association module is used for extracting semantic features and time sequences of dialogue history based on the state value and generating a context association parameter set; the intention recognition module is used for analyzing the relationship between the user intention and the context according to the context parameter set and outputting an intention recognition parameter set; the answer generation module is used for generating multiple rounds of answer generation values in combination with the knowledge base and the real-time semantic data; the semantic prediction module is used for predicting a semantic trend through a large model, analyzing context change and outputting a semantic prediction value; and the dialogue feedback module is used for performing error analysis according to the semantic predicted value, optimizing answer generation and finally outputting a multi-round dialogue automatic optimization scheme. According to the invention, the accuracy, coherence and adaptive ability of multiple rounds of dialogues can be improved.

The invention provides a fluororubber field knowledge graph construction method based on a large language model, and the method comprises the steps: obtaining process related knowledge data of the fluororubber field, and carrying out the classification, and obtaining the classification knowledge data of the fluororubber field; based on a preset ChatGLM model, performing knowledge extraction according to the fluororubber field classification knowledge data to extract key entities and relationships; carrying out deep analysis on the key entity and the relationship by utilizing semantic correlation and context association analysis, and enhancing the connectivity of the key entity and knowledge in the relationship through semantic similarity and association strength to obtain fused knowledge data; explaining and expanding the fused knowledge data by using a ChatGLM model to obtain expanded triple data; and performing knowledge fusion on the triple data, and performing graph storage by using a Neo4j database to obtain a knowledge graph. According to the method, the knowledge graph construction efficiency is remarkably improved through automatic knowledge extraction and processing of the large language model, and the time cost of manual labeling and data processing is reduced.

The invention relates to the technical field of ocean data interpolation filling, in particular to a space-time deficiency filling method and system based on context association and physical guidance. The method comprises the following steps: acquiring seawater dissolved oxygen data and context data; multivariable space-time dependence extraction is carried out based on the obtained seawater dissolved oxygen data and context data; gaussian noise diffusion is carried out based on the obtained seawater dissolved oxygen data; noise prediction is carried out based on double-view space-time correlation; and the prediction error is constrained based on the joint loss function. According to the method, a physical consistency constraint mechanism based on a partial differential equation is introduced in a model training process, so that model output better conforms to a physical coupling rule among variables in a marine environment. The constraint effectively inhibits non-physical fluctuation possibly occurring in the interpolation result, enhances the physical credibility and interpretability of the result, and provides a more reliable data basis for subsequent scientific analysis and process modeling.

The invention belongs to the technical field of public safety monitoring, and particularly relates to an intelligent monitoring system and method based on vision and language fusion. According to the method, multi-dimensional spatial analysis is carried out on a space-time sequence image, hierarchical feature information can be extracted, context association analysis is carried out on text data, semantic coding information is generated, then image features and text entity description are in butt joint through a multi-stage interaction channel of the hierarchical feature information and the semantic coding information, and the text entity description is extracted. And performing trend consistency calibration, eliminating phase deviation on a time sequence, predicting a future trend of hierarchical features and semantic coding, generating joint characterization data, dynamically correcting evaluation boundary parameters by combining environment feedback parameters on the basis, and finally evaluating the current joint characterization data according to the corrected evaluation boundary parameters. And determining the public safety state level in the target scene, thereby realizing the intelligent monitoring process of the public safety information in the target scene.

The invention provides a knowledge base construction method and an intelligent question and answer method based on a large language model. The knowledge base construction method comprises the steps of dividing multi-granularity texts, recursively merging the texts and then extracting feature vectors, and constructing a tree-shaped / net-shaped structure of the texts with different theme / abstract levels from a bottom layer to the top. According to the method, the problems of semantic depth and association in reading are solved by constructing a recursive tree structure, and granularity information of details is considered while wide subject understanding is kept; the nodes are allowed to be subjected to grouping optimization based on semantic similarity, and then the context association problem is solved in a mode of organizing text data in a tree-shaped / net-shaped structure; in the retrieval stage, the large model recall the multi-granularity text from the tree-shaped / net-shaped structure, and recall information is integrated.

The embodiment of the invention provides an intelligent factory semantic decision generation method and system based on a knowledge graph, and the method comprises the steps: obtaining an operation data set of an intelligent factory, carrying out the semantic feature extraction of the operation data set, and generating the target semantic feature of an equipment operation parameter and the context correlation feature of a semantic description text; and based on a pre-constructed knowledge graph structure, performing dynamic semantic matching processing on the target semantic features and the context association features, generating a semantic decision instruction set corresponding to the equipment operation parameters, generating an equipment control strategy set according to instruction priorities and instruction execution conditions in the semantic decision instruction set, and sending the equipment control strategy set to a server. And feeding back the equipment control strategy set to a control system of the intelligent factory to trigger operation optimization operation, and updating a semantic node association relationship in the knowledge graph structure. According to the method, the problem of fragmentation of equipment operation state representation is effectively solved, and the interpretability of feature extraction is improved by utilizing a collaborative verification mechanism of numerical parameters and text description.

The invention provides a tumor patient clinical test matching system and method based on a large language model and an OCR technology, and is applied to the field of medical data processing. The method comprises the following steps: analyzing clinical data and test information, processing an unstructured text, and generating structured clinical feature data through context association analysis; key data is extracted and subjected to double verification correction, and structured data supplementary information is generated; enhancing the structured clinical feature data and supplementary information based on a multi-modal processing assembly line module, extracting an image quantitative index, analyzing an immunohistochemical result, and generating a comprehensive matching score; through a rule engine and semantic similarity calculation, item-by-item comparison of patient features and entry and exhaust conditions is realized, and a preliminary matching result is generated; edge case misjudgment is corrected through context-aware multi-round reasoning, sorting is adjusted in combination with clinical test priority weights, and an optimized clinical test matching list is generated; and generating a clinical test matching report based on the data.

The invention provides an information resource matching recommendation method and system based on context awareness, and the method comprises the steps: firstly obtaining a context data set which is generated by real-time interaction of a target user and comprises user operation behavior data and scene awareness parameter data, and then carrying out the demand feature extraction of the context data set, the method comprises the following steps: acquiring a dynamic demand feature and a context association feature, matching the dynamic demand feature and the context association feature with a pre-stored information resource library to generate a real-time information resource matching result set, and optimizing a matching strategy of a target user matching model in real time according to a dynamic feedback parameter of the real-time information resource matching result set. According to the method, a resource matching strategy is optimized to obtain an optimized resource matching strategy, and finally, a target information resource set related to the dynamic demand characteristics is pushed to a target user terminal device based on the optimized resource matching strategy, so that more accurate and personalized information resource recommendation can be realized, and the recommendation quality and the efficiency of obtaining effective information by a user are improved.

The invention discloses an alarm information research and judgment noise reduction method. According to the method, multiple pieces of alarm information with internal relation are combined into a structured alarm cluster through deep mining and by utilizing context association information between alarm events, so that redundant information is greatly compressed, and the alarm signal-to-noise ratio is improved. And multi-dimensional and comprehensive evaluation is carried out on the alarm cluster by fusing three mechanisms of information entropy analysis, customization rule evaluation and semantic evaluation of a large model, so that high threat alarms can be identified more accurately. Wherein the information entropy analysis helps to identify potential unknown threats by quantifying the uncertainty of the alarm, and the large model semantic evaluation can deeply analyze the alarm content, so that the accuracy and efficiency of alarm analysis are improved.

The invention, which relates to the technical field of artificial intelligence, discloses an artificial intelligence-based education evaluation and feedback system comprising a data acquisition module, a vector generation module, a prediction module, an error region positioning module and a feedback module. The system constructs a unified high-dimensional cognitive state vector by collecting answering behaviors, eye movement tracks, facial micro-expressions, voices and intonations and electroencephalogram signals of students; generating a learning evolution path map based on a dynamic Bayesian network and a causal reasoning mechanism, and predicting future learning bottleneck nodes; an error region is recognized through semantic deconstruction and graph matching, and context-associated personalized feedback content is generated in combination with a generative language model; according to the system, an evaluation feedback closed loop of cognitive state modeling, accurate identification of an erroneous region and intelligent feedback pushing is realized, and the accuracy of education evaluation and the effectiveness of intervention are improved.

The invention belongs to the technical field of data analysis, and provides a questionnaire-based sentiment analysis method, system and equipment in order to solve the problem of inaccurate user sentiment analysis in the existing questionnaire. Using a pre-trained language model to extract a semantic vector of the topic text, and combining with the co-occurrence frequency of the multiple topic options to generate node-level local features; carrying out dynamic reasoning by adopting an improved graph neural network model, calculating a dynamic attention coefficient based on semantic similarity calculated by a topic text semantic vector and a jump probability between topics, and weighting and aggregating neighbor features, so as to obtain context-associated topic node features; the global emotion is calculated by using the PageRank thought, a final emotion analysis result is obtained, dynamic context association generated due to questionnaire jump logic is effectively captured, and the reliability of grasping the overall emotion venation of the user is improved.

The invention relates to the technical field of natural language processing, and discloses a workflow calling method based on a large language model. The method comprises the steps of receiving an initial task description text of a user, and decomposing the initial task description text into a discrete intention unit set through a semantic analysis engine; inputting a pre-trained large language model to carry out context association analysis, and generating a task node topological graph containing a hierarchical relationship; detecting a data transmission dependency relationship between nodes, and marking a strong association cluster with bidirectional data streams; according to cluster dynamic load parameters, automatically dividing parallel execution domains and distributing independent data transmission channels; and monitoring channel throughput fluctuation in real time, and triggering a channel switching protocol when the channel throughput is blocked. According to the method, the complex task execution logic and the data flow path are straightened out, the problems of dependency conflicts and resource allocation are solved, the orderliness and stability of workflow execution are guaranteed, and the automatic task scheduling requirements under various complex scenes are met.

The invention belongs to the field of natural language processing, and particularly relates to the field of large language models, in particular to an education large model training method under a fine-grained weighted reward mechanism. The method comprises the steps of collecting vocational education field data training strategy models and cleaning data; after completing the training of the base large model, training a sentence-level fine-grained reward model by using supervision data, and designing a context-associated scoring mechanism; after scoring model training and large model supervision fine tuning are completed, large model alignment is carried out, and the strategy is updated according to the total reward value. According to the method, the context factor and the dimension interaction factor are introduced, so that sentence scoring is more reasonable and effective, the safety and reliability of a large language model in the vocational education field are improved, and the method has wide application prospects.

The invention belongs to the field of artificial intelligence, and particularly discloses an AI virtual character intelligent generation effect analysis and evaluation method, which comprises the following steps of: firstly, comparing a test text with an expected reply, evaluating a semantic comprehension degree, and ensuring that vocabularies are accurate and similar to sentence structures; secondly, historical dialogues are analyzed, context relevance is evaluated, and communication coherence is ensured; by combining the two items, the language understanding ability is comprehensively judged, and the deficiency is warned in time. Meanwhile, the response duration is monitored stably, and smooth user experience is ensured. In addition, collapse times in a historical period are tracked, operation stability is evaluated, and faults are prevented. Finally, multi-dimensional data such as language understanding, response speed and operation stability are integrated, the intelligent generation effect is comprehensively evaluated, a scientific basis is provided for performance optimization and problem early warning of the virtual character, it is ensured that the overall performance of the virtual character in the application meets the expectation, and potential problems are early warned in time.

The invention provides a PDF document intelligent word segmentation method based on content structure perception, and belongs to the technical field of computer information.The PDF document intelligent word segmentation method comprises the steps that firstly, by fusing layout analysis and multi-tool analysis, multiple types of content such as title levels, ordinary tables, characters in pictures and tables in the pictures in a PDF are accurately recognized and extracted; secondly, providing a structure-perceived word segmentation strategy, and carrying out differentiation processing according to content types, namely, dividing semantic blocks by taking a title as a guide, taking a complete table as an independent semantic unit, and carrying out context association on a picture OCR (Optical Character Recognition) result; and finally outputting high-quality knowledge fragments rich in metadata such as levels and types. According to the method, the information integrity and semantic accuracy of the PDF document during construction of the large model knowledge base can be remarkably improved.

The embodiment of the invention relates to the technical field of data processing, and particularly provides a smart library information retrieval method and system based on AI session interaction. According to the embodiment of the invention, an information retrieval session request initiated by a user through a smart library interaction interface is received; collecting session interaction data containing natural language query content and context association information; performing semantic structuring processing on the session interaction data to generate a session semantic feature set containing an entity relationship network and an intention classification vector; carrying out deep mining on the session semantic feature set by utilizing a debugged retrieval intention understanding model, identifying potential retrieval demands and demand priority ranking, and generating retrieval demand feature vectors; and on the basis of library collection information in the vector matching intelligent library resource database, generating an information retrieval result set containing resource association degree sorting, and feeding back the information retrieval result set to the interactive interface for visual display. According to the embodiment of the invention, the accuracy and comprehensiveness of information retrieval are improved, and more intelligent and efficient retrieval experience is provided for users.

The invention discloses an AI Agent-based low-code outbound verbal skill process configuration method and system, and the method comprises the steps: carrying out the word segmentation and semantic analysis of an original verbal skill text through a verbal skill configuration Agent, recognizing the type of a core business intention, extracting key entity information, carrying out the feature extraction and weight calculation of customer portrait data, and generating a customer feature model, fusing the key entity information with the customer feature model to generate an optimized verbal skill suggestion; performing keyword scanning, semantic rule matching and context association analysis on the optimized verbal skill suggestion through a compliance detection Agent to obtain a latest compliance standard update and generate a compliance detection result; carrying out automatic replacement and risk prompt supplement on violation contents through a dynamic correction function, and generating a compliant version verbal skill; and performing format standardization processing on the verbal skill of the compliant version through the outbound system interface, and then pushing the verbal skill to an outbound execution platform. According to the invention, the problems of low verbal skill configuration efficiency and insufficient compliance detection capability in the prior art are solved.

The invention provides a low-altitude economic education intelligent question answering method and system for an interactive learning platform, and relates to the technical field of natural language process.The low-altitude economic education intelligent question answering method comprises the steps that firstly, a low-altitude economic education question data set containing multiple pieces of user interaction data is obtained, and a question feature set of the user interaction data is extracted; comprising semantic vector features, domain association features and context association features, retrieving a domain knowledge feature set in a low-altitude economic education knowledge base based on a question feature set, performing cross-dimensional feature fusion processing on the question feature set and the domain knowledge feature set, generating an optimized response statement of the user question statement, and sending the optimized response statement to the low-altitude economic education knowledge base. Finally, according to the difference characteristics of the optimized response statement and the historical response statement, the response service strategy of the interactive learning platform is dynamically adjusted, and the question and answer accuracy and the platform adaptability are improved.

The invention relates to the technical field of artificial intelligence, can be applied to business scenes such as financial science and technology and medical health, and discloses an interaction intention-driven tool scheduling method, device and equipment and a medium. The method comprises the steps of obtaining to-be-processed interaction data of visual data or text data, executing cross-modal semantic analysis to generate a unified interaction intention, and sending the unified interaction intention to a server; detecting intention association strength between the matching tool and the context state data, selecting a context association mode based on the strength, determining a target dialogue node, scheduling the matching tool to perform sandbox execution to obtain a tool execution result, generating response content, and sending the response content to the server; and selectively updating the to-be-processed interaction data, the unified interaction intention, the response content, the tool execution result, the target dialogue node and the extracted service entity data to context state data. According to the method, the interaction accuracy is improved through cross-modal analysis and a switchable context association mechanism, tool decoupling is achieved in a sandbox execution mode, and the system adaptability and stability are enhanced.

The invention discloses a semantic understanding and question-answering matching method and system, and the method comprises the steps: semantic question-answering information collection, database data processing, semantic understanding optimization processing, retrieval enhancement generation, and answer generation and quality control, i.e., processing a data set after data preprocessing by using a natural language; and extracting semantic features of queries and documents based on a deep learning model, capturing and determining a mapping relationship between the context information and the long-distance dependency relationship, selecting core field information in the context information, generating a set of core fields, and then generating high-quality question and answer data. According to the method, the deep learning model is used for extracting semantic features of queries and documents and capturing context information and a long-distance dependency relationship, so that context correlation analysis of core fields is realized, a matching generation mechanism of answers is improved, high-quality answer information is generated, and the accuracy and efficiency of semantic analysis are improved.