3669results about "Text database indexing" 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 an archive knowledge base construction and retrieval method and system based on multi-modal data fusion. The method comprises the steps that heterogeneous archive data are cleaned, image features are extracted through CNN, text features are extracted through Transform, audio is converted into text and then subjected to similarity, a unified feature vector is generated, and metadata is constructed according to archive code association; creating a graph database instance, defining nodes and relationship types, importing entities and relationships, and storing feature vectors and metadata; the features are mapped to a high-dimensional shared semantic space, positive and negative sample pairs are constructed to update embedded layer parameters, self-attention is used in modalities, a shared attention mechanism is used between modalities, weights are adjusted according to archive features, and unified knowledge representation is generated; segmenting the steering quantity of the multi-modal data, storing the steering quantity into a database, and adopting hierarchical indexing and optimizing as required; related document fragments are retrieved through RAG technology vectors, answers are generated with the help of a large language model, and session feedback is provided. The file retrieval efficiency and accuracy are improved.

The invention relates to a multi-data standard definition conflict resolution method based on a large model and a knowledge graph, and belongs to the field of public data governance and data standardization. The method comprises the following steps: extracting and preprocessing a multi-data standard definition; defining semantic representation and large model analysis: performing semantic analysis on the definition of each data item by using a large model, and generating a semantic embedding vector and a key feature; knowledge graph construction and entity alignment: constructing a knowledge graph containing all data items, and connecting data item nodes with similar semantics in different standards by adopting an entity alignment algorithm to form a candidate alignment relationship; conflict detection and type identification: aiming at the aligned data item definition, comparing attributes and values, identifying definition conflict points, and classifying and marking conflict types; and carrying out conflict resolution and unified definition generation by using a globally optimized conflict resolution algorithm. According to the method, the problems of data islands and semantic conflicts caused by inconsistent data standard definitions in the prior art are solved.

The invention provides an agent-based large language model retrieval enhancement generation system and method, and the system comprises a planning layer which is used for receiving user query, carrying out the multi-round iterative decomposition of a complex task through a task planning agent, and generating an atomic query or a direct response; the execution layer is used for executing the atomic query generated by the planning layer in parallel, calling a search module to obtain external knowledge base data, and caching an intermediate result through a memory module; the answer detection module is used for performing multi-dimensional detection on the generated result, including preference, accuracy, integrity and logicality; the dynamic decision-making module is used for adaptively adjusting a subsequent retrieval strategy and a task planning process according to a detection result and user feedback; and a cross-layer interaction mechanism enables the planning layer and the execution layer to realize collaborative optimization through context sharing and iterative feedback.

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 discloses a reverse question guide question answering implementation method and system, and belongs to the technical field of artificial intelligence and natural language processing. Context-aware intention dynamic correction is realized through a three-level intention classification system, and cross-modal knowledge matching is realized by adopting a distributed semantic index technology; based on the reinforcement learning strategy, optimizing a cooperative work mechanism of the dialogue strategy and the knowledge base; comprising the steps of intention recognition: analyzing a session of a user by using an intention recognition model, and constructing a three-level intention classification system based on deep semantic understanding, including main class recognition, fine-grained analysis and context perception; question rewriting: constructing a dynamic rewriting engine to rewrite the user question; recalling and cleaning multi-source item knowledge; generating a reverse question; locking items and acquiring item data; generating questions and answers. According to the method, the robustness, the real-time performance and the scene adaptation capability of a professional question answering system can be improved, and the government affair service question answering accuracy, the intention recognition precision and the cross-region recommendation adoption rate are improved.

The invention provides an RAG-based text block dynamic segmentation method and system, and the method comprises the steps: analyzing a multi-level directory structure of a document, and segmenting a text block by taking a directory node as a reference; for a document without a directory or with an incomplete directory, a mixed segmentation mode of rules and semantics is switched; performing potential Dirichlet distribution topic modeling on the continuous text stream, and calculating a topic distribution vector of each text segment in real time; performing adaptive segmentation on the detected topic boundary, inserting a hard segmentation mark at a topic mutation point, and performing soft segmentation on a gradual change topic area; the initial window size of the sliding window is set according to the document type, and the semantic density in the window is monitored in real time; and layering, blocking and recombining. The system comprises a segmentation mode module, a mark confirmation module and a block recombination module. According to the method, the RAG retrieval accuracy is improved, the memory occupation is reduced, and meanwhile, the streaming throughput is supported.

The embodiment of the invention provides a government affair digital human dynamic interaction method and system based on a multi-mode large model. The method is applied to the technical field of government affair intelligent services, and comprises the following steps: acquiring a policy announcement text, and performing cleaning and structuring processing to obtain a structured policy data set; and extracting old and new policy data, performing difference comparison, marking key change fields, and generating policy change data. Abstracting and element extraction are carried out on the change data to form structured semantic fragments, and the structured semantic fragments are incrementally embedded into the policy knowledge graph. And generating a question and answer pair sample based on the updated knowledge graph, and carrying out self-supervised fine tuning training on the multi-modal large model. A user inputs multi-modal data, and the model generates a government affair response and feeds back the government affair response. According to the scheme, the multi-modal large model can continuously keep the latest policy knowledge; the model is enabled to generate accurate government affair response with consistent context while understanding multi-modal input such as text, voice and image, and timeliness, accuracy and interactive experience of policy interpretation are improved.

A data processing system implements receiving a call requesting a generative model to generate a semantic tree for a source content; constructing a first prompt including the source content and instructions to the model to analyze a semantic structure of the source content and to generate a semantic outline and content chunks of the source content, the semantic outline including one or more topics each connected with one or more of the content chunks, to compute one summary for each of the content chunks, to apply indices to reference each topic node of the semantic tree to one of the topics, and to apply indices to reference each leaf node of the semantic tree to one of the content chunks and the respective summary; providing the first prompt to the model and receiving the semantic tree of the source content; and storing the semantic tree in a database.

The invention provides an intelligent agent tool calling knowledge optimization method based on empirical path graph evolution, which comprises the following steps: when an intelligent agent successfully completes a task for the first time, recording an intelligent agent tool calling sequence, input and output parameters and an execution result, generating a structured calling log, the calling log is converted into a standardized calling path knowledge unit; performing structured representation and semantic representation on the calling path knowledge unit, storing the structured representation in a graph database, and storing the semantic representation in a vector database; task intentions, tool entities and calling paths are used as heterogeneous nodes, an experience path knowledge graph is constructed, the experience path knowledge graph is used for recording the multi-dimensional relation among tasks, paths and tools, execution performance attributes and feedback attributes are added to path nodes in the graph, and agent tool calling knowledge optimization is completed. And the purpose of improving the tool calling efficiency and robustness of the intelligent agent in the multi-task environment is achieved.

The invention belongs to the technical field of natural language processing and information retrieval. The invention provides a bidding document multi-modal duplicate checking method and system based on a large model, and the method comprises the steps: carrying out the structural analysis and multi-modal feature extraction of an input bidding document, and generating the feature representation of semantic blocks and non-text elements; performing deep semantic coding on text blocks by using a dynamic context-aware large language model, and retaining logic relevance of a long text in combination with hierarchical position coding; efficient matching of massive semantic vectors is achieved through a mixed retrieval framework, and calculation efficiency is optimized in combination with a distributed calculation framework and a hardware acceleration instruction; and finally, performing structured feature reconstruction on non-text contents such as tables, charts and the like to realize cross-modal semantic association analysis.

The invention provides a multi-model collaborative knowledge graph construction method, system and device and a storage medium, and the method comprises the steps that a routing engine receives a knowledge graph construction request, matches a preset rule base according to target domain parameters, and generates a node assembly sequence; the scheduling engine constructs a task execution directed acyclic graph according to the task execution directed acyclic graph; the execution engine preprocesses the original document according to the graph to generate a structured document block set with metadata; calling a pre-training model in the small model resource pool to perform entity and relation extraction to form a preliminary entity set and an association relation set; attribute completion and implicit relation reasoning are carried out on the preliminary entity set, and a completion entity attribute set and a newly-added relation set are generated; performing entity alignment on the preliminary entity set and the complemented entity attribute set by the large model to obtain a fused entity set; and performing conflict resolution on the incidence relation set and the newly-added relation set to obtain a fusion relation set. And storing the fusion entity set and the fusion relationship set to a knowledge graph database.

The invention provides a multi-modal heterogeneous model retrieval enhancement method and system, and the method comprises the steps: building a knowledge and application example double-corpus based on user multi-modal query, and designing a joint retrieval mechanism to obtain a result set; mapping and scheduling to obtain feature representation through special processing channels for texts, images and audios and a Spiking neural network with a segmented trapezoidal topological structure; constructing a three-stage cascade architecture of a basic model, an advanced model and human experts, and obtaining a decision path and answer candidate set in combination with a recursive and discarding decision mechanism; a Hamiltonian graph network is used for representing a multi-modal relation, and a gradient-free descent method is used for rapidly training and optimizing model parameters; an enhanced retrieval result is obtained through cross-modal semantic alignment and dynamic retrieval window adjustment; and high-quality response is obtained through context-aware sorting and retrieval enhanced reasoning. According to the method, the multi-modal information retrieval processing efficiency and the heterogeneous model reasoning response quality are improved.

The invention relates to the technical field of artificial intelligence and machine learning, in particular to a government affair industry intelligent information retrieval and pushing system and method based on a large model, and the system comprises an intelligent semantic understanding and query analysis module, a semantic-driven efficient retrieval module, a generation-enhanced intelligent content generation module, and a personalized pushing and feedback optimization module. The method has the beneficial effects that a natural language query request input by a user is received through the intelligent semantic understanding and query analysis module, semantic analysis and intention recognition are performed by utilizing a pre-trained large language model, key information is extracted, and query logic is optimized through a context sensing mechanism. Then, a semantic-driven efficient retrieval module quickly retrieves document fragments most relevant to user query from mass data of government affair cloud, precise matching is achieved through semantic vectorization and an efficient vector retrieval technology, and a retrieval strategy is dynamically optimized in combination with user feedback.

The invention discloses a talent matching method and system based on artificial intelligence, and aims to improve human resource configuration efficiency and decision intelligence. The method comprises the following steps: collecting talent and demand data in multiple channels, especially unstructured communication data including interview records and work communication records; processing data through technologies such as multi-mode resume analysis, extracting features and fusing the features; an enterprise talent knowledge base which is used for continuous learning and dynamic maintenance based on a system operation result and multi-source feedback and integrates structured and unstructured data is constructed; based on the natural language query of the user, providing intelligent question answering and decision support by using a retrieval enhancement generation model connected with the specific enterprise talent knowledge base; an advanced deep learning algorithm is adopted, a knowledge base is combined to carry out man-post matching and generate recommendation of context perception, and self-optimization of a matching strategy is realized through mechanisms such as reinforcement learning and the like. According to the invention, accurate and dynamic talent matching and intelligent decision making can be realized.

The invention provides an intelligent data query method based on a natural language, and relates to the technical field of intelligent data processing and natural language interaction.The intelligent data query method comprises the steps that firstly, enterprise original data is subjected to standard treatment, and a standardized theme database and a data directory and index definition document are constructed; key semantics are extracted based on unstructured knowledge, and a domain knowledge vector library is fused and constructed by combining document text fragments and vector representation of a mapping relation between historical questions of a user and an SQL (Structured Query Language). And after receiving a natural language question of a user, calling a large language model to identify a task type, and distinguishing knowledge questions and answers, data query and complex analysis. Executing corresponding operations according to different types: directly retrieving a vector library by knowledge questions and answers to generate answers; extracting keywords in data query and generating a query request in combination with context; and in the complex analysis, predefined workflow is judged and executed or intelligent agent processing is called, and query or analysis requirements are output.

The invention relates to the technical field of artificial intelligence, can be applied to service scenes of pension service, financial science and technology, medical health and the like, and discloses an instruction understanding and task execution method, device, equipment and medium, and the method comprises the steps: receiving a voice instruction and a text instruction, and carrying out the cooperative processing through an instruction understanding model, and generating a structured task description; collecting environment data to construct a real-time environment model; generating a task execution strategy by utilizing a task execution model based on the structured task description and the real-time environment model; controlling the intelligent agent to execute the task according to the task execution strategy, and dynamically adjusting the action in combination with real-time sensor information; task execution data and user feedback information are collected, and the instruction understanding model and the task execution model are updated. According to the method, multi-modal information is fused through structural description, an execution strategy is generated in combination with real-time environment perception, actions are dynamically adjusted, model self-optimization is further achieved through execution data and feedback, and the understanding, decision-making and adaptive capacity of an intelligent agent is improved.

Provided in the embodiments of the present disclosure are a question-answering processing method, and a device, a product and a storage medium. In the question-answering processing method, after a query instruction is acquired, target knowledge information that matches the query instruction can be acquired from among a plurality of pieces of knowledge information in a knowledge base, and the query instruction and content-parsed text that corresponds to the target knowledge information are input into a large language model for question-answering processing, wherein the content-parsed text that corresponds to the target knowledge information is obtained by means of performing content parsing on a target document element that corresponds to the target knowledge information, and when the target document element comprises a document element of a non-text modality, content parsing is performed on the target document element before the target document element is input into the large language model, such that the document element of the non-text modality in the target document element can be understood by the large language model, so as to provide question-answering reference knowledge with a relatively high reliability for the large language model. Therefore, the accuracy of answering of the large language model for the query instruction can be improved.

The invention belongs to the field of question and answer generation, and provides an interactive retrieval enhancement question and answer generation method and system based on a knowledge graph, and the method comprises the steps: carrying out the document partitioning based on an original document set, generating a global block set, carrying out the entity extraction of each text block in the global block set, and obtaining an entity set; performing relation extraction on entity subsets in each text block in the entity set to obtain a global relation set; generating a plurality of sub-knowledge maps based on the global block set, the entity set and the global relationship set, and performing entity fusion and relationship fusion on the sub-knowledge maps to obtain a knowledge map; performing keyword extraction and semantic embedding on the original problem to obtain a dense vector, performing semantic embedding based on the knowledge graph to obtain an embedded vector, and generating a candidate entity set according to the dense vector and the embedded vector; and based on the candidate entity set, utilizing a large language model calling tool to carry out extended search to generate a candidate information set, and utilizing a large language model to obtain an answer to the original question based on the candidate information set.

The invention discloses a multi-modal fused intelligent question answering and knowledge retrieval method and system, and the method comprises the steps: building a multi-modal data index model oriented to a heterogeneous knowledge source, carrying out the feature mapping of text, image, table, chart, audio and video contents through a unified semantic embedding space, and generating a cross-modal index set; after a query request is received, performing semantic matching and structure matching on the cross-modal index set by using a multi-channel retriever to obtain candidate evidence fragments; and based on an evidence granularity decomposition strategy, performing minimum evidence unit division on text statements, table units, chart data points and multimedia frame contents in the candidate evidence fragments, and establishing a semantic consistency graph among the units. According to the method, high-credibility traceable generation of question and answer results is realized through multi-modal fusion and space-time consistency constraint, and the retrieval precision and interpretation transparency in a complex knowledge scene are remarkably improved.

The invention relates to the technical field of artificial intelligence and robots. According to the multi-modal perception and interaction method and device in the body environment, the method comprises the steps that environment entropy estimation processing is carried out through a dynamic weighting multi-modal feature fusion algorithm, and an environment entropy value representing the disorder degree of the environment is generated; performing cross-modal alignment processing to generate a fusion environment understanding map; performing dynamic decision processing through the task adaptive reinforcement learning model to generate an interaction instruction; driving an execution mechanism to execute the interaction action to obtain an execution result of the interaction action; carrying out dynamic adjustment processing on the weight of the environment entropy value to generate an updated environment entropy weight; and performing local knowledge node incremental updating processing on the meta-knowledge base to generate an optimized meta-knowledge base so as to solve the problems of insufficient consistency of cross-modal data and poor environmental understanding robustness caused by large distribution deviation between virtual features generated by a generation model in a noise or data missing scene and a real environment in related technologies.

The invention provides an intelligent marketing copywriting generation and effect evaluation method driven by a large language model, relates to the technical field of language models, and comprises the steps of constructing a hierarchical cross-modal knowledge graph and establishing a knowledge retrieval index. Semantic analysis is performed based on a bidirectional attention mechanism, related knowledge is retrieved by using query vectors, and an initial marketing copywriting is generated. Performing knowledge consistency verification to generate a knowledge-enhanced marketing copywriting, and generating a candidate copywriting set by adopting a diversity sampling strategy; and performing knowledge coverage, creativity and expected effect scoring on the candidate copywriting by using a multi-target evaluation network, screening an optimal copywriting through a Pareto optimization algorithm, and taking a generated path of the optimal copywriting as a positive sample to update a knowledge graph and decoder network parameters. Through knowledge graph enhancement and multi-target evaluation optimization, high-quality and high-matching-degree marketing copywriting can be generated, and the marketing effect is improved.

The invention discloses an enterprise scientific and technological achievement adaptation method based on big data accurate retrieval and query, and relates to the technical field of big data processing and knowledge maps, and the method comprises the steps: constructing an enterprise technical field knowledge map based on a standardized multi-modal feature matrix, receiving incremental technical data in real time by adopting an Apache Flink streaming processing framework, and carrying out the real-time retrieval and query of the enterprise technical field knowledge map. Dynamically updating a graph node relationship, removing expired nodes through a pruning algorithm, and outputting a dynamic knowledge graph with a timestamp; and receiving a query request of a user, extracting a query vector by utilizing the cross-modal embedding model, retrieving a Top-N candidate node list through a dynamic knowledge graph, calculating a comprehensive similarity score in combination with a graph relation weight, and outputting a sorted technical achievement list. Multi-source heterogeneous data is mapped to a unified semantic space through a cross-modal embedding model, and the intelligent level and the actual application effect of scientific and technological achievement adaptation are comprehensively improved in combination with construction and updating of a dynamic knowledge graph.

The invention provides an intelligent medical question-answering system and method based on hybrid retrieval and lightweight reordering, and is applied to the technical field of medical data processing. According to the method, five types of core entities are extracted through the preset Chinese medical NER model, and the structured knowledge base is formed through relation extraction modeling association. Analyzing user Chinese query, extracting medical entities, identifying four types of appeals and converting the four types of appeals into semantic vectors; a PubMedBERT is adopted to encode a medical document to generate a vector for storage, BM25 and vector retrieval are executed in parallel when query is received, and candidate documents are generated through fusion of an RRF algorithm. And generating a score data set based on the prompt template, and training the lightweight model to sort and output an evidence set. In combination with query semantics, a simplified context is generated through retrieval, sorting and compression, and FlashAttention optimization calculation is integrated. An optimized U-Net segmentation image is utilized to generate a structured report, and multi-modal information is integrated to generate an accurate answer giving consideration to the image and medical knowledge through LLM reasoning.

Provided is a process including: obtaining, with a computer system, access to a code base; decomposing, with the computer system, the code base into parts; generating, with the computer system, documentation for the parts with a language model; associating, with the computer system, the documentation with the parts; indexing, with the computer system, the documentation; obtaining, with the computer system, a query searching for content in the code base; searching, with the computer system, using the index, the code base based on the generated documentation to identify documentation corresponding to the query and, then, content in the code base associated with the identified documentation; and responding, with the computer system, to the query, by identifying the content in the code base associated with the identified documentation.

A computing services environment may include a database system storing a plurality of database records for a plurality of client organizations accessing computing services including a conversational chat assistant. The computing services environment may also include an application server may receive user input for the conversational chat assistant, a generative language model interface, an orchestration and planning service configured to identify one or more actions based on the user input, to execute the one or more actions to determine a natural language response message, and to determine a recommended action for selection via a conversational chat interface. The computing services environment may also include a communication interface configured to transmit the natural language response message and a user interface generation instruction executable by the client machine to provide a selection affordance for selecting the recommended action.

Techniques for logical text passage generation and retrieval for retrieval-augmented generation. The techniques involve processing markup language documents to generate logical text passages and their corresponding embeddings. These embeddings are indexed for efficient retrieval. Upon receiving a user utterance, a user query is formed and transformed into an embedding to query the index. Relevant text passages are identified and used to prompt a large language model (LLM), which generates a completion. This completion is then sent as a response to the user. The process effectively bridges user queries with relevant information through advanced embedding and natural language processing techniques, enabling accurate and contextually appropriate interactions within a user-agent dialogue framework.

The invention discloses a wind turbine generator operation and maintenance knowledge base construction method based on a large model and mechanism self-learning. The wind turbine generator operation and maintenance knowledge base construction method comprises the steps of wind turbine generator operation and maintenance domain knowledge Schema definition and large model cue word template design used for wind turbine generator operation and maintenance knowledge extraction; obtaining operation and maintenance multi-modal data of the wind turbine generator, performing preprocessing, and performing knowledge extraction through a large model based on a designed cue word template; a dynamic knowledge association and wind turbine generator operation and maintenance knowledge base fault mechanism self-learning updating mechanism is established, operation and maintenance data and a knowledge graph are associated in real time, and the knowledge base is automatically learned and updated through an exception triggering mechanism; and constructing and storing a wind turbine generator operation and maintenance knowledge graph based on a knowledge extraction result, generating a semantic association sub-graph through clustering, generating a sub-graph clustering report, and realizing efficient knowledge retrieval. Based on the above content, the wind turbine generator operation and maintenance knowledge base which is efficient, accurate and updated in real time is constructed.

The invention relates to the technical field of big language model reasoning, and discloses a big language model key value cache multiplexing method and system based on fuzzy semantic matching, and the method comprises the steps: generating a key value cache according to lexical elements in a historical reasoning request of a user, gathering a plurality of lexical elements into lexical element blocks, generating embedded vectors of the lexical element blocks, and building a vector database; calculating a cosine similarity between an embedded vector of a lexical block of a new reasoning request and a historical embedded vector in a vector database, and if a historical lexical block of which the cosine similarity exceeds a set threshold exists, obtaining a corresponding key value cache through a Hash index and multiplexing the key value cache; calculating an attention score, and dividing the plurality of lexical elements in the current lexical element block into keyword elements and non-keyword elements based on the attention score; key value caches of the keyword elements are recalculated; and the re-calculated key value cache of the keyword elements and the reuse key value cache of the non-key sub-elements form a mixed key value cache. According to the method, on the premise that the model precision is almost not reduced, the key value cache multiplexing technology is expanded to fuzzy semantic matching from accurate matching, unnecessary calculation overhead is effectively reduced, and then the reasoning efficiency is improved.

The invention relates to a document processing method and system based on text content extraction. The method comprises the steps that an original document containing text, image and format information is received, the encoding format of the document is automatically detected, character set conversion is executed, and hierarchical indexes including page numbers, paragraphs and tables are established for an unstructured document; the method comprises the following steps: synchronously processing text content and visual layout through a pre-trained visual-language model, extracting word-level and sentence-level semantic features by a text stream embedding layer, analyzing spatial distribution features of document elements by a visual encoder, and fusing text and visual features through a cross-modal attention mechanism; and loading the domain knowledge graph matched with the document type, and executing entity linking to associate the text mentions to the knowledge nodes. According to the document processing method and system based on text content extraction, through the synergistic effect of vision-text joint coding and knowledge enhancement, the accuracy of financial contract key clause recognition tasks is improved, the error rate is lower than that of industry benchmark products, and the semantic understanding precision is remarkably improved.