4645 results about "Index term" patented technology

The invention relates to the technical field of database construction, in particular to a knowledge base question-answering system construction method and device, equipment and a storage medium. The method comprises the following steps: acquiring a user question instruction, and performing part-of-speech tagging and keyword vectorization on a target text to generate a retrieval text keyword vector; performing semantic linear embedding and spatial projection processing on the keyword vectors to obtain keyword semantic vectors; logic mining is conducted on the keywords, a logic relation set among the keywords is obtained, logic recognition is conducted on the target text based on the relations, and retrieval text logic is generated; performing matching retrieval with semantic vectors in a knowledge base to obtain a candidate semantic query data set; and performing coherent logic retrieval on the candidate data set according to the retrieval text logic and the keyword vector to generate a final retrieval target answer. According to the method and the system, more accurate and efficient knowledge base question-answering system construction is realized.

The invention discloses a method for enhancing dynamic knowledge retrieval based on a large language model, belongs to the field of knowledge retrieval, and aims to solve the problems of knowledge solidification, insufficient timeliness and illusion of a traditional LLM (Logistics Language Model). A multi-granularity knowledge base is dynamically constructed, and a rule and semantic partitioning technology is combined, so that a text is converted into a normalized vector, and a hybrid index is established; a two-channel retrieval triggering mechanism is adopted, keyword matching scores and BERT semantic probability analysis are fused, and retrieval requirements are intelligently judged; vectorization retrieval is realized through a BGE-M3 model, and candidate results are reordered in combination with a cross encoder to improve the precision. The system supports multi-language adaptive processing, dynamic switching of word segmentation strategies and cross-language retrieval, and introduces real-time knowledge updating and version control. According to the method, the answer timeliness and accuracy are remarkably improved, the context coherence of multiple rounds of dialogues is optimized, the method can be widely applied to the fields of intelligent customer service, professional questions and answers and the like, the LLM illusion risk is effectively reduced, and the knowledge traceability is enhanced.

The embodiment of the invention discloses a retrieval generation method and device based on a large language model and a knowledge graph. The problems that retrieval semantic relevance is weak, and recalled data lacks global knowledge can be solved. The method comprises the following steps: performing vector analysis on a plurality of preset domain documents, and constructing a vector database; constructing a knowledge graph according to the entity description information and the relationship description information extracted from the plurality of field documents, and storing the knowledge graph into a graph database; performing feature extraction on a target question input by a user to obtain a multi-dimensional keyword corresponding to the target question; retrieving the multi-dimensional keywords through a preset search engine, a vector database and a graph database to obtain a target retrieval result; and determining target input information according to the target retrieval result, and inputting the target input information into the target language model to obtain target output response information.

The invention belongs to the technical field of artificial intelligence, and relates to a power document intelligent question and answer method and system based on a large language model. The method comprises the steps of obtaining document data according to a preset period; preprocessing the document data to obtain an adaptive document; constructing a vector retrieval library through an embedded model according to the adaptive document; obtaining a user query question, inputting the user query question into a vector retrieval library, performing retrieval based on a vector similarity retrieval method and a keyword retrieval method to obtain related text blocks, and screening out the most related text block from the related text blocks through a two-stage rearrangement technology; a user queries the question and the most relevant text block and inputs the question and the most relevant text block into the large language model to obtain preliminary intelligent answer information; and correcting the preliminary intelligent answer information based on a preset self-provincial inversion mechanism to obtain final intelligent answer information. Multiple rounds of questions and answers and context understanding are supported based on a correction mechanism, context consistency can be kept in continuous query of complex technical questions, and the accuracy of multiple rounds of questions and responses is ensured.

The invention discloses a knowledge base enhancement generation method and system based on hybrid retrieval and fact verification. The method comprises the following steps: receiving an original query of a user; performing intention analysis and rewriting on the query, identifying an intention type and generating a sub-query adapted to retrieval; executing mixed retrieval of vector retrieval, keyword retrieval and selectable knowledge graph retrieval based on the sub-query, and recalling related knowledge fragments; performing deduplication clustering, fact conflict recognition processing and correlation reordering on the knowledge fragments; according to the intention type and the reordered knowledge fragment, dynamically selecting a prompt template to construct an enhanced prompt; and sending the enhanced prompt into the large language model, and generating a target response with the reference source. According to the method, knowledge recall comprehensiveness is improved through mixed retrieval, knowledge reliability is ensured through fact verification, generation logicality is enhanced in combination with dynamic prompt construction, and response accuracy and credibility are improved.

The invention provides an intelligent retrieval method and system for unstructured asset content based on a large model, and relates to the technical field of data retrieval, and the method comprises the steps: carrying out semantic expansion on retrieval keywords through a knowledge graph enhancement module, obtaining asset feature vectors through a multi-modal feature extraction model, and constructing a hierarchical asset feature index database; the comprehensive similarity is calculated through a weighted fusion algorithm, and retrieval results are optimized and reordered through semantic comprehension and correlation analysis capacity of the pre-trained large model. According to the method, the retrieval precision and efficiency of the unstructured asset content are improved, and intelligent association analysis and semantic understanding of cross-modal assets are realized.

The invention discloses a hybrid enhanced index and system based on vector retrieval and a BM2 algorithm, and the method comprises the steps: uploading a document by a user, analyzing the document by the system to obtain text content, segmenting the text content, converting the segmented text content into dense vectors, storing the dense vectors in a vector library, extracting keywords of segmented texts by using a large model, processing the keywords, and inserting the keywords into a word list. Word segmentation is carried out on the text based on the constructed word list and a word segmentation device; in response to the received user query, vectorizing the user query, and calculating the similarity with each vector in the vector library to obtain a preliminary query result; performing word segmentation on user query, performing keyword retrieval by using a BM25 algorithm according to a word list and a word segmentation device to obtain query results, and filtering the similarity of the two query results by the reordering model according to a set threshold value; selecting a result with the highest similarity according to large model parameter limitation and an upper limit set by business requirements; and splicing the document content corresponding to the result and the user query into a cue word, inputting the cue word into the large model, and analyzing a semantic relationship and a logic structure in the cue word to generate an answer.

The invention discloses a building electromechanical BIM model information rapid retrieval method and system, and the method comprises the steps: generating composite retrieval parameters fusing semantic keywords and three-dimensional coordinate constraints according to a multi-mode retrieval instruction inputted by a user; on the basis of the composite retrieval parameters, constructing a dynamic search space by utilizing a hierarchical graph convolutional network, and generating a candidate model index structure of multi-dimensional feature coding; inputting the candidate model index into a multi-objective optimization engine, performing real-time optimization on a search path by adopting a dynamic pruning algorithm driven by reinforcement learning, and outputting a candidate model set of which the confidence coefficient is higher than a preset confidence threshold after pruning; and on the basis of the candidate model set, associated equipment nodes are expanded through a knowledge graph embedding and complementing technology, and an enhanced retrieval result set containing the hidden associated equipment is generated. By utilizing the embodiment of the invention, efficient, multi-dimensional and multi-modal information accurate positioning and quick retrieval can be realized in a large-scale complex BIM model.

The invention discloses a multi-modal interview automatic quality analysis and evaluation method and system based on a large model, and the method comprises the steps: collecting and storing multi-modal data, such as texts, audios, videos and behavior interaction, and carrying out the preprocessing of the multi-modal data to form a standardized data set; utilizing a preset interview structure and a large model to dynamically guide the process, adjusting the topic rhythm according to real-time feedback, and recording stage conversion information to form logic trajectory data for process coherence management; automatically coding text data through a large language model, extracting features such as keywords and performing topic clustering, performing cross validation and semantic fusion in combination with data analysis results of each modal, and generating deep analysis results such as psychological states; and generating a comprehensive assessment report containing qualitative description, quantitative score and psychological abnormality or cognitive disorder risk prompts based on a deep analysis result, thereby providing a basis for psychological health assessment and cognitive competence evaluation. According to the method, automatic analysis of multi-modal data is realized, and evaluation scientificity and efficiency are improved.

The invention discloses a knowledge base generation and retrieval method based on the RAG technology and an intelligent question answering system, and belongs to the technical field of natural language processing, and the knowledge base generation and retrieval method based on the RAG technology and the intelligent question answering system based on the RAG technology have the beneficial effects that the accuracy and the real-time performance of information processing are improved through efficient combination of retrieval and generation. High-dimensional vector representation is generated through text slicing and vectorization, a vocabulary index is constructed, and hierarchical retrieval and keyword matching are achieved; meanwhile, a question generation and question-question retrieval method is utilized, a result is optimized and generated in combination with a large language model (LLM), the reasoning ability of the large model is further improved through chain reasoning (COT) and a cue word optimization module, and it is ensured that the generated answer has high accuracy and logicality; the method can be widely applied to the fields of intelligent question answering, knowledge base construction, real-time information retrieval and the like.

The invention relates to the technical field of data processing, in particular to a method and system for processing data based on a large language model.The method comprises the following steps that based on inter-sentence punctuation positioning and part-of-speech tagging, sentence blocks are divided to generate functional partitions, themes and relational words are extracted to judge semantic chain starting points, a trigger index sequence is constructed, and logic jump points and breakpoint positions are recognized; and mapping the label structure to the language model output analysis deviation, and generating a label mapping combination list. According to the method, semantic turning nodes can be captured by analyzing the semantic direction change trend, the relation between the semantic turning nodes and verb and noun combinations is judged, the position of a trigger point of an actual information transfer effect is extracted, and the break point area of a semantic path is recognized through the positioning of key word starting and stopping blocks and the logical judgment of noun group cross combination; the path integrity has a clear fracture identifier, a traceable semantic mapping structure path is established in a language model, and the accuracy, coherence and hierarchy clearness of semantic reconstruction are enhanced.

The invention relates to the technical field of document retrieval and information processing in the data processing technology, in particular to a document retrieval method based on multistage indexing and feature clustering, which comprises the following steps: performing high-dimensional space mapping on multi-modal features such as texts and images through a quantum embedding layer to generate cross-modal joint feature representation; a first-level index of a multi-level index architecture is dynamically initialized based on a meta-clustering algorithm, and semantic blocks of a second-level index are divided in combination with a multi-head self-attention mechanism. And an optimal transmission matrix is generated by using a Sinkhorn algorithm to align cross-node feature distribution. The multi-target mixed retrieval strategy is fused with vector retrieval, keyword retrieval and graph retrieval results, and weight distribution is dynamically adjusted. Through collaborative optimization of quantum calculation, federated learning and causal reasoning, a closed-loop technical architecture from feature analysis to dynamic index construction is formed, the problems of insufficient cross-modal fusion, static clustering deviation and semantic association deficiency are solved, and the precision, efficiency and dynamic adaptability of heterogeneous document retrieval are improved.

The invention provides a prospecting prediction method based on a multi-agent technology. The method specifically comprises the steps that S11, keyword extraction is conducted on mineral problems input by a user; s12, establishing a vector database of the mineral knowledge text and a graph database of the mineral knowledge graph, and optimizing the extracted keywords to generate cue words; s13, constructing a large prospecting model to think the cue words, expanding the cue words, and obtaining a complete statement for professional expression of the user question again; s14, performing multi-task decomposition on the complete statement to obtain a plurality of sub-tasks, and scheduling the plurality of sub-tasks to corresponding agents based on a graph database of the mineral knowledge graph to perform step-by-step prediction; and S15, integrating prediction results of the intelligent agents and feeding back the integrated prediction results to a user. According to the method, prospecting prediction is promoted to be converted from manual driving to intelligent driving, and emerging technical support is provided for prospecting breakthrough.

The invention discloses an enterprise-level knowledge base construction method based on a large model. The enterprise-level knowledge base construction method comprises the following specific steps: acquiring original data of an enterprise and preprocessing the original data to obtain first target data; performing knowledge extraction and representation based on the first target data to obtain second target data; based on the random-degree large model and the second target data, performing knowledge graph construction and storage to obtain an enterprise-level knowledge base; performing knowledge retrieval and recommendation on the enterprise-level knowledge base by using a modular retrieval enhancement generation method; and periodically updating and maintaining the enterprise-level knowledge base. According to the method, keywords and concepts in various knowledge carriers such as internal documents, reports and communication records of enterprises can be automatically identified based on the strong analysis capability of the random-degree large model, and automatic association among knowledge elements is realized; the enterprise-level knowledge base is quickly constructed, the knowledge base is periodically updated and maintained in time, the content of the enterprise-level knowledge base is enriched, and a foundation is laid for subsequent enterprise knowledge search.

The invention provides a data reordering retrieval method and system based on RAG, and the method comprises the steps: carrying out the dynamic semantic partitioning processing of an original document, generating corresponding document blocks, storing the document blocks in a vector database, constructing a hierarchical index, analyzing a received query request, extracting keywords in the query request, and carrying out the retrieval of the query request. Boolean keyword matching is carried out through an inverted index in the hierarchical index, semantic retrieval of keywords is carried out in a vector database, an initial candidate set is generated after multi-source retrieval results are fused, a query request and the initial candidate set are input into a generative reordering model, and a query result is obtained; according to the method, the initial candidate set is selected, the corresponding score is generated through the cross-modal attention mechanism in the generative reordering model, the initial candidate set is reordered according to the score, the final retrieval result is obtained, and the reordered document blocks are displayed, so that the overall retrieval speed is increased, the limitation of a single retrieval mode is avoided, and the accuracy of the retrieval result is improved.

The invention relates to a low-altitude intelligent question and answer construction method and system based on dynamic parameters. The method comprises the following steps: collecting low-altitude domain data, cleaning the low-altitude domain data, generating a semantic vector index, and constructing a low-altitude domain knowledge base based on the semantic vector index; receiving a natural language query of a user, analyzing a query intention, extracting keywords in the natural language query, and matching a corresponding candidate word quantity based on query types of the natural language query of the user, the query types at least comprising high-frequency phrase query and low-frequency long-tail query; and respectively carrying out fusion semantic retrieval and keyword retrieval, carrying out secondary sorting on the candidate results based on a preset resorter, preferentially sorting the candidate results related to the query intention, and outputting the corresponding candidate results. By adopting the method, a combined domain retrieval enhancement generation mechanism is provided, so that the professionality and accuracy of answers are improved; and the retrieved knowledge base content is re-screened to increase the hit probability of the knowledge base.

The invention relates to the technical field of data analysis, can be applied to business scenes of medical health, financial science and technology, culture research and the like, and discloses a knowledge graph-based model analysis method, which comprises the following steps of: acquiring and processing target domain data, extracting and labeling keywords, constructing a knowledge graph comprising domain entities, a relational network and attribute characteristics, and analyzing the knowledge graph. Establishing a professional corpus and generating an analysis task, constructing a multi-dimensional analysis index system, inputting the analysis task to a to-be-analyzed model, obtaining a task output result, performing knowledge verification according to the knowledge graph, evaluating the knowledge verification result based on a multi-dimensional analysis index, and generating a final analysis result. According to the method, by fusing the knowledge graph and the multi-dimensional analysis indexes, the accuracy and comprehensiveness of evaluation are enhanced, the evaluation effect of the model in the aspects of professional knowledge understanding, reasoning ability and standard integrating degree is improved, the evaluation result is more reliable, and the application ability of the large model in the specific field is optimized.

The invention provides a multi-knowledge-granularity text retrieval method and device for RAG, and the method comprises the steps: sorting an original text into a JSON file, carrying out the recursive character and semantic dicing processing, generating a target JSON file, and storing the target JSON file in a BM25 database and a vector database; when a user queries, preprocessing to generate a keyword list and a query embedded vector; searching for a target text in the BM25 database, and calculating a score of correlation with the keyword to determine a first to-be-selected text; determining a second to-be-selected text according to the query embedding vector; and performing duplicate removal processing on all the first to-be-selected texts and the second to-be-selected texts, and determining and outputting a final recommended text by utilizing a sorting model. By considering the problem of different search granularities, the keyword granularity is solved by adopting a BM25 algorithm. According to the semantic retrieval strategy combining sentence granularity and segment granularity, challenges of different text information lengths and different retrieval purposes are effectively handled, and therefore the advantages of traditional retrieval and semantic retrieval are fully utilized.

The invention provides a large model training data synthesis method, system and device and a storage medium, and the method comprises the steps: carrying out the preprocessing of original military corpus data, generating a semantic vector through a pre-training language model, dividing the semantic vector into a plurality of topic clusters through clustering analysis, extracting the keyword and representative sample of each topic cluster, forming a theme ontology library; selecting associated topic pairs based on the topic similarity matrix, and generating a synthetic sample through a predefined template; controlling the language style of the synthetic sample through the cue word instruction, and generating a stylized sample; dividing difficulty grades of the stylized samples to obtain graded samples; performing multi-dimensional quality evaluation and screening on the graded samples to obtain screened samples; the screened samples are used for training a large language model, and according to the performance of the large language model in each theme task, theme weights are adjusted, a synthesis strategy is updated, and a sample structure is optimized. According to the method, high-quality and diversified training data can be provided for large model training.

The invention relates to a knowledge-enhanced large model data analysis agent visualization method, which comprises the following steps of: performing syntactic analysis by using a PCFG algorithm, identifying syntactic components and relationships of sentences, and splitting the sentences into sub-sentences with complete logic; extracting keywords and entity words of the sentences by combining a word segmentation technology with a business word segmentation library; recommending and summarizing candidate segmented words through a recall strategy, carrying out rough arrangement, fine arrangement and fine arrangement on the candidate segmented words, and correcting and complementing a segmented word result; splitting and analyzing the natural language problem based on the fine-tuned large model, and generating SQL statements meeting business requirements; analyzing the data set generated by the SQL, extracting field information and business meanings, and identifying an association relationship between fields; and automatically recommending chart styles according to data features and user preferences, assembling an analysis report and distributing the analysis report according to a specified frequency. According to the method, knowledge enhancement and large model technologies are combined, so that the efficiency and effect of data analysis and visualization are remarkably improved.

The invention discloses a multi-modal literature data extraction method and device and a medium. The method comprises the following steps: firstly, standardizing literatures to obtain a structured document comprising a literature directory, author information, a paragraph text, a paragraph layout, a table LaTeX code, a formula LaTeX code and / or image description; then, related retrieval of fragments is performed on texts, formulas, tables and images from the structured document according to keywords and / or vectorization retrieval strategies. The retrieved fragments are optimized through correlation sorting, and the number of the fragments needing to enter large model processing is reduced. And finally, performing question and answer configuration on the large language model based on a user extraction demand and a sorting fragment, generating a structured answer through a prompt engineering technology, ensuring that the results can be traced through an original fragment, ensuring the availability of question and answer results, and reminding the user to verify and process a part which cannot be traced when necessary.

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 risk defense method and device for jailbreak attack of a large language model. Risk defense is carried out from the input side, the model side and the output side of the large language model. The risk defense method comprises the following steps: S1, receiving input text information; s2, establishing a dynamic malicious keyword library on the input side of the large language model; s3, on the model side of the large language model, based on the training data transmitted in the step S2, training and learning model operation feedback information and model internal state information, and based on a dynamic adjustment strategy, adjusting the large language model; and S4, constructing an output auditing library on the output side of the large language model based on the pre-output text information transmitted in the step S3 and the dynamic malicious keyword library in the step S2. Through the cooperative work, the defense measures of each layer can play the maximum effect in the whole security framework, so that various potential attacks are effectively prevented and dealt with, and the overall defense capability of the model is jointly improved.

Embodiments of the present disclosure provide a method and system for generating a document, a computing device, and a medium. The method comprises: receiving a prompt input by a user; obtaining a first keyword from the prompt; using a vector representation of the prompt, retrieving, from a vector index database, enhancement information associated with the prompt and a second keyword associated with the enhancement information; on the basis of comparison of the first keyword and the second keyword, using the enhancement information to generate an enhanced prompt; and providing the enhanced prompt to a language model to generate a document.

The invention discloses a contract auditing method, apparatus and device, and a computer readable storage medium. The method comprises the steps of obtaining an image file corresponding to a contract file; inputting the image file into a multi-modal analysis model for model identification so as to obtain text information of each text area corresponding to the image file; performing screening operation on the text information based on a preset keyword to obtain target text information; and auditing the target text information by adopting a large language model based on an entity recognition rule to obtain an auditing result. According to the method, the text information is screened, the target text information is audited on the basis of the large language model, intelligent audit of the contract file is achieved, meaningless input of the large model and the data annotation process can be reduced as much as possible through keyword screening and entity recognition rule matching, and the user experience is improved. The efficiency and accuracy of contract auditing are improved.

The invention belongs to the field of artificial intelligence and finance, and relates to a text classification method, which comprises the following steps: preprocessing an original text data set, labeling classification labels to obtain a labeled text data set, inputting a training set into a large language model for keyword extraction to obtain a candidate keyword set, cleaning the candidate keyword set to obtain a classification keyword set, constructing an instruction template based on the classification keyword set, and inputting the training set and the instruction template into a large language model to obtain a prediction classification result; and based on a prediction classification result and a loss function between the classification labels, performing fine tuning on the large language model to obtain a fine tuning model, evaluating that the fine tuning model is qualified to obtain a text classification model, and inputting the to-be-classified text into the text classification model for classification. The invention further provides a text classification device, computer equipment and a storage medium. In addition, the invention also relates to a block chain technology, and the tagged text data set can be stored in a block chain. According to the method, the text classification accuracy and stability can be improved.

The invention provides a minority language professional question and answer method based on mixed retrieval and retrieval enhancement generation, which comprises the following steps: S1, constructing a multi-language knowledge constructing a vector index database and a term knowledge graph by using a multi-language model according to a related minority language document; s2, multi-layer mixed retrieval: cross-language document recall is realized through a multi-layer mixed retrieval module, and the multi-layer mixed retrieval module is composed of keyword retrieval, semantic retrieval and vector retrieval; s3, answer generation: performing answer generation through an adaptive multi-language model by using a retrieval enhancement generation module, and introducing rule constraint decoding and a dynamic attention mechanism in the generation stage to improve the professionality and accuracy of the answer; and S4, self-adaptive optimization and knowledge updating: through a user feedback reinforcement learning module, optimizing the model based on user error correction data and supervising updating of the knowledge base. According to the method, professional questions and answers of the minority language can be realized based on a small amount of professional data of the minority language, the recall rate of the document of the minority language is improved, and the generation quality is improved.

The invention discloses a text keyword desensitization method, system and equipment based on a large model and a medium, belongs to the technical field of large model and natural language processing, and aims to solve the technical problem of how to automatically identify and desensitize sensitive information, improve the accuracy of sensitive information identification and further improve the text processing efficiency. According to the technical scheme, the method comprises the steps of data collection; preprocessing the data; marking sensitive information; segmenting the data; selecting a basic model; finely adjusting the model; training the fine-tuned large language model, and obtaining and storing the trained fine-tuned large language model; preprocessing the to-be-processed text data; detecting sensitive information, formulating a desensitization strategy and implementing desensitization processing, performing desensitization verification on the text after desensitization processing, and optimizing and improving the fine-tuned large language model according to a verification result; and packaging the optimized and improved fine-tuned large language model into an API (Application Program Interface) service for an application program to call.

The invention relates to the technical field of artificial intelligence, and discloses a rapid labeling method based on an artificial intelligence large model, which comprises the following steps of: 1, extracting an entity set and a relationship set of a specific field through a document set of a target field, identifying nouns, terms and keywords related to the field in a document by using an entity identification technology to form an entity set, and marking the entity set and the relationship set; the method comprises the following steps of: establishing an entity set, analyzing logic and semantic relevance among entities by utilizing a relation extraction technology, generating a relation set, combining the entity set with the relation set to construct a knowledge triad, and the knowledge triad comprises an association relation among the entities. By introducing the domain knowledge graph, the entity and relation information extracted from the target domain document is structured into the knowledge triad, and the knowledge triad is matched with the to-be-annotated data to optimize the annotated semantic information, so that the domain adaptability and semantic consistency of the annotation result are realized, and higher accuracy and consistency effects of the annotation data in the professional field are obtained.

The invention provides a method for creating a structured document based on a deep learning model, and relates to the technical field of data processing, and the method comprises the steps: obtaining basic text information collected by a user, obtaining text data, fragmenting the text data, obtaining text fragmented data, and creating a structured document according to the text fragmented data. Analyzing the structures of the sentences in the paragraphs, determining keywords in the sentences, analyzing the logical relationship between the sentences in the paragraphs, associating the sentence keywords in the paragraphs, analyzing the association degree between the paragraphs in the chapters, identifying core keywords of the chapters, obtaining a chapter keyword data set, and analyzing the field involved in the text. The method comprises the steps of extracting field information of a text to obtain a text keyword data set, constructing a structured document model based on a graph neural network model, and inputting the text keyword data set and text data into the structured document model to obtain a structured document. According to the method, the structured document can be constructed through the deep learning model.