928 results about "Named-entity recognition" patented technology

The invention relates to a multi-modal named entity recognition method based on cross-modal guide interactive fusion, which comprises the following steps: constructing a data set, designing a cross-modal contrast aggregation mechanism, respectively extracting image features and text features, and constructing a contrast learning mechanism to screen out image features with high association degree with text semantics for dynamic aggregation; a DINO model is introduced to extract image features, a dynamic similarity matching method is constructed, a dynamic similarity matching weight is generated based on text features and an image feature correlation matrix, a dynamic gating mechanism is utilized to adaptively select image features related to text feature context, and a cross-modal fusion and guide interaction strategy is constructed. Outputting an enhanced semantic representation vector, and mapping the semantic representation vector after multi-modal fusion into a final entity tag sequence by adopting a conditional random field decoder to complete entity recognition; the method has the advantage of remarkably improving the robustness and accuracy of multi-modal entity recognition.

A schema and cell value aware Named Entity Recognition (NER) model is used to perform natural language queries. Natural language queries may be received via an interface of a natural language query processing system. A fuzzy search may be performed that allows non-exact matches for column names or cell values of data sets potentially used to answer the natural language query. An NER model that adds a type embedding for an exact match of a column name or cell found in the fuzzy search that corresponds to a span of one or more words may be applied as part of generating the entity prediction for the natural language query. One or more queries to at least one of the data sets may be performed to return a result to the natural language query using the entity prediction generated by the NER machine learning model.

The invention discloses a multi-modal entity and relation extraction method and system based on cross-modal alignment and fusion, and the method comprises the steps: carrying out the processing and coding of an input text and an image, and obtaining a plurality of types of image and text features; performing feature alignment on the fine-grained and coarse-grained text features and the pixel-level image representation by taking the semantic representation of the image as an anchor point, and mapping the image and the text features to the same semantic space; performing multi-granularity feature fusion through text-guided dynamic gating aggregation, visual prefix cross-modal fusion and cross-modal image-text matching, modeling association between noun phrases and image objects in a text while increasing feature complementarity, and obtaining multi-granularity multi-modal feature representation; fusing multi-granularity multi-modal features through entity guidance attention gating, and gathering visual information related to a text entity to obtain final multi-modal fusion representation; according to the multi-modal fusion representation, task prediction of multi-modal named entity recognition and multi-modal relation extraction is carried out.

The invention discloses a power transformer fault auxiliary decision-making method and system based on a knowledge graph and a large language model, and the method comprises the steps: obtaining power transformer fault text data, carrying out the preprocessing, obtaining fault-related text and table data, preliminarily defining an ontology, selecting a part of text data for marking, and carrying out the recognition of the ontology; a plurality of named entity recognition and relation extraction models are trained, an optimal model is determined, then triple extraction is carried out on unlabeled text data, and a knowledge graph is constructed; predicting a potential entity relationship in the knowledge graph based on a link prediction model, and complementing the knowledge graph under the large language model and human assistance; constructing a semantic search model training data set based on the large language model and training a semantic search model; based on the complemented knowledge graph, the trained semantic search model is utilized to retrieve knowledge graph sub-graphs according to the problem, and auxiliary decision making is completed. According to the method, the problem of low potential knowledge utilization level in the existing power transformer fault auxiliary decision-making based on the knowledge graph is solved.

The invention discloses a large model scheduling multi-agent power grid fault coping strategy knowledge graph extension method and system, and the method comprises the steps: carrying out the modeling of task distribution as a mixed integer programming problem, and achieving the solving through a relaxation-correction algorithm; extracting and normalizing a core task of knowledge, and outputting structured data; summarizing the obtained text segments, and prompting a large language model to retain key entities, relationships and domain-specific terms; using an LLM-based named entity recognition technology, combining with prompt and dictionary / ontology filtering in the power dispatching field, recognizing related entities in a text, and normalizing the related entities into a standard form in a knowledge graph; detecting logic contradictions between the newly extracted triples and existing relationships in the knowledge graph, and classifying and solving the contradictions by utilizing debate prompts based on LLM (Logistics Library Model); summarizing the plurality of verification signals, and calculating the global confidence, definition and correlation score of each triple;

Aspects of the present disclosure provide techniques for enhanced electronic data retrieval. Embodiments include receiving a natural language query and identifying one or more electronic data sources indicated in the natural language query using a named entity recognition (NER) machine learning model trained through a supervised learning process based on training natural language strings associated with labels indicating entity names. Embodiments include determining one or more additional electronic data sources related to the one or more electronic data sources using a knowledge graph that maps relationships among electronic data sources. Embodiments include retrieving data related to the natural language query by transmitting requests to the one or more electronic data sources and the one or more additional electronic data sources and providing a response to the natural language query based on the data related to the natural language query.

The invention provides a structured processing method and device for a contract text, equipment and a storage medium, and the method comprises the steps: carrying out the preprocessing of the contract text, generating a standardized word sequence, and enabling the standardized word sequence to comprise a plurality of word sequences and part-of-speech tagging information of each word sequence; the standardized word sequence is input into a named entity recognition model to recognize key entities in the contract text, key terms in the contract text are extracted based on a machine learning algorithm, the key entities include both parties of the contract, the amount of the contract and the name of a product, and the key terms include payment terms, delivery terms, default responsibility terms and force majeure terms; through dependency syntactic analysis and a semantic role labeling model, analyzing to obtain a semantic relationship between the key entities and the key terms; and storing the extracted key entities, key terms and semantic relationships in a target database in a structured form, and establishing a target index to support quick query. According to the invention, the error rate of manual processing is reduced.

The invention discloses a relation extraction method and system based on a graph neural network, and belongs to the technical field of natural language processing. A target text is obtained, word segmentation, part-of-speech tagging and named entity recognition are carried out, and an entity set is extracted; constructing a text graph structure containing multiple edge types based on the entity set; performing feature coding on nodes in the graph to generate an initial feature vector fusing semantic, part-of-speech and position information; inputting the graph into the graph neural network model, and obtaining high-order node representation through multi-layer message passing and aggregation; modeling the entity pair in combination with the structure path and the context information, and inputting a multi-channel classification network to predict the relationship type of the multi-channel classification network; and finally, outputting an entity relationship triple according to a prediction result. The method has stronger semantic modeling ability and structure expression ability in a relation extraction task, and is suitable for scenes such as knowledge graph construction and information extraction systems.

The invention belongs to the technical field of natural language processing and multi-modal information extraction, and particularly relates to a multi-modal named entity recognition method based on semantic alignment and cross-modal graph fusion, which comprises the following steps: S1, acquiring a data sample containing a text sequence and image content; s2, encoding the text and the image into vectors respectively; s3, similarity is calculated through a trainable bilinear function, and optimization is carried out through loss comparison; s4, cross-modal attention is used to enhance association information between modals; s5, determining the proportion of reserved image information through a modal matching module; s6, introducing a gating mechanism to dynamically fuse visual and text features; s7, realizing local and global information complementation by a cross-modal graph fusion model; and S8, inputting the fused representation into the CRF layer to predict the entity type. According to the method, fine semantic alignment can be realized in a weak image-text correlation context, and balance between local entity recognition and global semantic understanding can be achieved.

In one embodiment, a method of training one or more artificial intelligence (AI) models for language-based communication prompts with a service provider is disclosed. The training method includes generating industry specific labels used to fine tune a large language model; providing an industry specific database associated with the industry specific labels to fine tune the large language model; reading the industry specific database into the large language model; adjusting the parameters of the large language model to recognize industry specific terms associated with servicing equipment within the industry; adjusting the parameters of the large language model to discover the intent associated with the industry specific terms; and adjusting the parameters of the large language model for industry specific tasks including questions and answer tasks, named entity recognition, classification tasks, and machine translations tasks.

The invention provides a translation semantic correction method and system based on a large language model. Belongs to the technical field of natural language processing. The method comprises the following steps: collecting data; performing named entity recognition by using a large language model, extracting specific entities from a source language text, and analyzing a relationship between the entities through LLM to generate an entity pair relationship; identifying and extracting attributes of the entities, and associating the attributes with the corresponding entities; integrating the extracted entities, relationships and attributes according to a graph structure, and constructing a preliminary knowledge graph; inputting the source language text into the model for translation; terms in the preliminary translation result are compared with the knowledge graph, and potential semantic deviation is recognized. And performing quality evaluation on the translation result. By integrating the identified and extracted relationship and attribute between the entities into the knowledge graph, the semantic relationship between the entities can be understood more deeply, so that the subsequent translation is more in line with the actual context.

The invention discloses an AI-based large-model-driven contract review and regulation interpretation method and system, and the method comprises the steps: S1, collecting a regulation and institute document, and carrying out the text extraction and semantic disassembly, and obtaining term information; s2, based on a LawCheckLM + BERT-CRF named entity recognition model, performing entity recognition and classification labeling on clause information, and storing recognized key information fields into a knowledge base; s3, encoding each piece of clause information into a semantic vector through an embedded model, and storing the semantic vector into a knowledge base; and S4, performing text extraction and semantic disassembly on the uploaded contract document to obtain clause information, repeating the steps S2-S3, retrieving similar semantic vectors of laws and regulations or institutional documents similar to the contract from the knowledge base as references, if the similar semantic vectors are not retrieved, taking Top-N laws and regulations or institutional documents with similar semantics as outputs, and marking the Top-N laws and regulations or institutional documents as required to be examined. The problems that an existing management system is difficult to adapt to regulation changes, complex contract review and cross-scene deployment are achieved, and the overall iteration period is long are solved.

The invention discloses a geological domain named entity accurate recognition and classification method based on thinking chain enhancement and hybrid expert architecture, which is characterized by comprising the following steps: firstly, extracting geological document text data through an OCR (Optical Character Recognition) technology, and extracting structured entity data by utilizing a locally deployed large language model; then calling a local large model to generate a diversified sentence pattern template according to language styles in the geological field, filling the template with the extracted professional entities, and constructing an instruction fine tuning data set; further constructing a thinking chain (CoT) enhanced data set on the basis, and explicitly simulating an expert reasoning process; efficient fine tuning is carried out on the large model by innovatively combining a low-rank adaptation (DoRA) technology and a hybrid expert (MoE) architecture, the DoRA technology carries out dimension reduction decomposition and orthogonal transformation on weight matrixes of a decoder layer and a multi-layer perceptron, and the MoE architecture constructs a plurality of special sub-networks to enhance the multi-task processing capability; and finally, performing entity extraction on the geological document by using the fine-tuned model, outputting an identification result containing a reasoning process, and filtering and perfecting the result through a rule matching mechanism. According to the method, the problems of fuzzy boundary, indefinite semantics, difficulty in classification and the like of the named entities in the geological field are effectively solved, the recognition and classification accuracy of the named entities in the geological field is remarkably improved, and key technical support is provided for downstream applications such as geological resource exploration and mineral evaluation.

The invention provides a mediation document generation method and system, relates to the technical field of data processing, and comprises the step of forming structured case data through text preprocessing, named entity recognition and legal information association based on case text data and legal corpus information. Then, generating a preliminary mediation document with placeholders by utilizing case classification, semantic matching and a sequence-to-sequence model; after standard auditing and logic auditing are carried out on the documents, optimization is carried out based on reinforcement learning, language style migration and a text generative adversarial network, and the structural rationality, law term normativity and semantic integrity are improved. And finally, a mediator preference template is constructed through Few-shot Learning and meta learning, the format and language style of the document are adjusted in a personalized manner, and a mediation document conforming to the habits of a mediator is generated. According to the method, the intelligence and the accuracy of mediation document generation are improved, the law compliance and the logic preciseness are ensured, and the mediation working efficiency is improved.

Artificially intelligent systems and methods for financial coaching provide personalized, fiduciary-compliant financial guidance through advanced machine learning architectures with measurable performance criteria. The systems implement privacy-preserving processing pipelines that detect personally identifiable information using multi-layered pattern recognition including regular expressions for formatted data sequences, named entity recognition with confidence thresholds above 0.85, and contextual analysis algorithms. A multi-step artificial intelligence processing workflow includes automated language detection, emotional tone classification with confidence scoring, financial profile transformation using predefined templates, context-aware question rephrasing, and semantic similarity matching employing vector embeddings with financial domain vocabulary weighting applying multiplier values between 1.3-2.0. Specialized training methodologies expand datasets through mathematical transformation functions utilizing statistical standard deviations with incremental variations between 0.5-2.0. Mood-based escalation logic automatically transfers users to human advisors when emotional indicators exceed confidence thresholds above 0.8. The systems maintain response times below 5 seconds while providing regulatory compliance through curated content sources and predefined fiduciary instruction parameters.

The invention discloses a natural language question and answer method, system and device, a medium and a product, and relates to the technical field of semantic recognition. The method comprises the steps that a user question is recognized based on a named entity recognition model, and a key entity is determined; the key entity comprises a place name, a distance and an orientation in the question; performing text classification on the user question based on a user intention recognition model, and determining the intention of the user; determining a structured query statement based on a preset language model according to the key entity and the intention; obtaining multi-dimensional geographic space data in the Internet map; converting the multi-dimensional geographic space data into a triple, and constructing a structured knowledge graph; and according to the structured query statement and the structured knowledge graph, determining an answer to the user question. According to the method, the intention of the question of the user is accurately and intelligently understood, and the accurate answer is returned.

The invention relates to a security management knowledge graph construction and dynamic updating method based on association modeling, and the method comprises the steps: collecting multi-source heterogeneous data in the field of security management, carrying out the recognition of the data type, carrying out the data conversion, setting an active learning mechanism in a named entity recognition model, carrying out the training, and recognizing an entity from the data. The entity is extracted; constructing an attention-enhanced graph neural network to analyze image data, calculating an attention weight for each node and edge in a message transmission process, and updating node information to realize relation extraction; based on the entity and relational data, constructing a preliminary security management knowledge graph; reasoning and supplementing missing information and relations based on an external standard knowledge base; the data change increment is detected in real time to update the security management knowledge graph, nodes and relationships are newly added or updated in the security management knowledge graph, the security management knowledge graph is efficiently constructed and dynamically updated, and support is provided for cross-modal intelligent question answering and real-time security evaluation.

The invention relates to the technical field of agricultural information, in particular to a tobacco agriculture standard named entity recognition method and system based on a hybrid neural network, and the method comprises the steps: carrying out the embedded representation of an input tobacco agriculture standard text through a BERT pre-training language model, and generating a word vector sequence containing global semantic information; performing local feature extraction on the word vector sequence by using an iterative expansion convolutional neural network to obtain a local feature vector; splicing the global semantic information and the local feature vectors, and inputting the spliced global semantic information and local feature vectors into a bidirectional long-short-term memory network for context feature extraction to generate context enhancement features; weight optimization is carried out on the context enhancement features through a multi-head attention mechanism, and key semantic features are highlighted; and performing label prediction on the optimized feature sequence by adopting a conditional random field decoder, and outputting a standard article element entity identification result. According to the method, high-precision and high-robustness named entity recognition is realized, and the method is particularly suitable for complex semantic and low-resource field scenes.

Knowledge graph guide and entity controlled techniques for generating synthetic doctor-patient conversations. In one particular aspect, a method is provided that includes obtaining an original dataset containing textual dialogue associated with a plurality of individual doctor-patient conversations for training a machine learning model, constructing input data by using named entity recognition to capture and categorize named medical entities present in the dialogue, generating prepared input data by arranging the input data in an annotated turn-by-turn conversation format using an input data preparation algorithm having various control parameters, training the machine learning model using the prepared input data, utilizing a knowledge graph to identify a plurality of symptoms mapped to a randomly selected disease, and causing the trained machine learning model to generate a synthetic doctor-patient conversation by inputting the plurality of symptoms to the machine learning model as a first control parameter of a conversation generation control algorithm.

The invention provides an electronic medical record management method and system based on AI calculation. The method comprises the following steps: designing a general template of an electronic medical record; preprocessing the patient data; dividing the preprocessed patient data into patient numerical data and patient text data; marking key content items in all the universal content items in the universal template; performing abnormal data detection on the patient numerical data and the patient text data based on the target universal template; extracting a patient data entity and a content item entity from the target electronic medical record by using a named entity recognition model; constructing a medical record content graph network of the target electronic medical record in combination with the patient data entity and the content item entity; and performing abnormal content detection on the target electronic medical record based on the medical record content graph network, and if the target electronic medical record passes the abnormal content detection, storing the target electronic medical record. The method has the effect of intelligently generating the high-quality electronic medical record.

The embodiment of the invention discloses a document knowledge element extraction method and device based on a large model and a medium, relates to the technical field of knowledge maps, and is used for solving the problem that an existing mode is poor in accuracy and low in interpretability. The method comprises the steps of obtaining original documents of a specified field based on a multi-source interface, and performing part-of-speech tagging and named entity recognition processing according to document contents and document fields of the original documents to obtain standardized documents; corresponding domain terms are collected, a corresponding domain knowledge graph is constructed, and entities and relationships of the domain knowledge graph are mapped to a low-dimensional vector space for data amplification; inputting a preset large model according to the amplified domain knowledge graph, and performing domain fine tuning on the preset large model based on a preset strategy to obtain a domain adaptive model; and inputting a to-be-extracted document in a specified field into the field adaptation model to extract knowledge elements of the to-be-extracted document, screening important knowledge elements based on the attention scores corresponding to the knowledge elements, and performing visual display.

The invention provides a large model named entity recognition method based on representative sample selection and context enhancement, which comprises a representative sample selection module, an entity knowledge construction module, a dynamic context selection module, a large model calling module and an iterative feedback optimization module, according to representative sample selection, samples with representativeness and information diversity are automatically selected from unlabeled data for labeling through a sample screening strategy based on clustering, entity description integration aims at each entity type, a plurality of high-quality instances are extracted from labeled samples, and standardized entity definition or description prompts are constructed. According to the dynamic context selection, for to-be-recognized text content, a context example most relevant to a target text is dynamically selected from a historical annotation sample or a description set through a semantic similarity retrieval mechanism to serve as auxiliary prompt input, and the adaptability and generalization ability of LLM in a complex or variable scene are improved.

The invention relates to a multi-modal fault diagnosis method based on cross-modal data enhancement, and the method comprises the following steps: obtaining an industrial multi-modal data set, inputting a multi-modal named entity recognition model, and obtaining a fault diagnosis result; in the multi-modal named entity recognition model, a cross-modal data enhancement module is established, and image description is generated according to image data to improve the diversity of training data, so that the robustness of the model in a low-resource scene is enhanced; constructing a global alignment optimizer based on an information theory principle, aligning global semantics among modals by maximizing consistency information among the modals, and filtering irrelevant visual noise by minimizing irrelevant information of image modals; a fine-grained cross-perception module is constructed based on a cross attention mechanism, accurate interaction between text and image modalities is captured, and multi-modal representation with rich information is generated, so that the accuracy and reliability of fault diagnosis are improved. Compared with the prior art, the robustness and accuracy of fault diagnosis can be further improved.

The invention discloses a Chinese named entity recognition system, method and equipment based on multi-scale features and a medium, and the method comprises the steps: decomposing an original text into a character sequence through a preprocessing module, and preprocessing characters, including removing punctuation marks and uniformly converting the characters into lower letters; a RoBERTa-WWM sub-module of a feature extraction module is responsible for converting an input text into a high-dimensional feature vector, a CNN sub-module effectively extracts local features through a sliding window mechanism, and a BiLSTM sub-module finally utilizes the advantage of bidirectional processing to obtain complete context information; decoding the hidden state representation by using a dynamic conditional random field (CRF) through a sequence tagging module to determine an optimal tag sequence; according to the method, pre-training, multi-scale feature fusion and dynamic decoding technologies are integrated, and efficient and accurate Chinese entity recognition is realized; through a unique preprocessing rule, a modular architecture design and optimized hyper-parameter configuration, the performance and robustness of the system in a complex scene are ensured.

The invention relates to a small-sample contrast enhancement fine tuning method and system based on a large language model, which are used for identifying named entities in recruitment texts. The method comprises the steps of performing cleaning and format conversion on an original recruitment text, and generating an input sample conforming to a natural language instruction format; under the condition that the labeled samples are insufficient, positive and negative sample pairs are constructed to enhance the recognition capability of the model on entity categories and boundaries; carrying out low-rank parameter updating on the pre-trained large language model by adopting a LoRA fine tuning technology, and reducing computing resource consumption in combination with 4-bit quantitative training; in a pre-training large language model reasoning process, through a multi-dimensional joint confidence evaluation mechanism, confidence of four dimensions of entity levels, lengths, types and contexts is synthesized, and low-confidence identification results are filtered after dynamic weighted normalization processing. The method is suitable for recruitment recommendation, talent matching and other downstream tasks, and has the advantages of high recognition accuracy, low training cost, high system robustness and the like.

The invention relates to the technical field of information extraction, in particular to an information extraction method and system for electronic archives. The method comprises the following steps: obtaining a to-be-processed electronic file and carrying out OCR identification to generate initial text data; error detection is carried out on the initial text data, and OCR error recognition candidate items in the initial text data are recognized; for each OCR misrecognition candidate item, generating a first data name according to context semantics and a layout structure of the candidate item; extracting low-level features of the first data name, and performing named entity recognition on each OCR misrecognition candidate item by utilizing a preset field word list in combination with the first data name; through a four-in-one process of ''misrecognition detection + named entity recognition + semantic error correction + templated extraction'', the core technology bottlenecks of inaccurate recognition, poor error correction capability, low information extraction intelligence and the like in the prior art are solved, and the accuracy, stability and intelligent level of electronic archive information extraction are remarkably improved.

The invention relates to the field of artificial intelligence, in particular to a long text segmentation information retrieval result coherence enhancement method, system and equipment based on a knowledge graph, and the method comprises the following steps: receiving a long text input by a user; segmenting the long text into a plurality of segments according to a preset length, and recording the original position and sequence of each segment; performing named entity recognition on each fragment, and extracting a key entity; querying related information in a knowledge graph according to the key entity, and obtaining attributes of the entity and a relationship between the attributes; integrating the related information obtained by query into the corresponding text fragment to form a context enhanced fragment; receiving a user query request; calculating a correlation score based on the context enhanced fragment and the user query, and retrieving a related text fragment by using a hybrid retrieval strategy; and selecting the related text fragment with the highest score to generate a final answer, and outputting the final answer to the user. Therefore, the information integrity and the context continuity in the retrieval process are ensured.

The invention particularly relates to an automatic index creating method based on a large model. According to the automatic index creation method based on the large model, after a user inputs a demand and carries out preprocessing, named entity recognition is carried out by utilizing the large language model, key entity information is extracted, and a structured demand parameter is generated; matching a template from the SQL template library and dynamically filling the template, and obtaining an SQL statement after grammar verification and performance optimization; connecting a data source through a database connection technology, executing the SQL statement, and obtaining an index calculation result; and checking the result, defining parameters of the API interface according to the interface specification, generating an interface document, and issuing the interface document. According to the automatic index creation method based on the large model, the working efficiency is greatly improved, the labor cost is reduced, meanwhile, index calculation errors caused by manual understanding deviation can be avoided, the accuracy of an index calculation result is ensured, the interface adaptation and development workload is reduced, the system integration difficulty is reduced, and the implementation is easy. And the data sharing and interaction efficiency is improved.

The invention relates to the technical field of data processing, in particular to an integrated multi-mode culture resource intelligent data governance and management system. The system comprises a data acquisition module, an intelligent processing module, a data management module, a knowledge organization module and an interactive display module. Synchronous acquisition of videos, audios, images and three-dimensional point clouds is realized through camera equipment, audio acquisition equipment and a laser scanner, and cultural elements are extracted through action recognition, speech recognition model processing dialect transcription, image semantic segmentation and named entity recognition by adopting a convolutional neural network. The system realizes structured description and semantic mapping of data on the basis of non-abandoned domain ontology, and constructs a knowledge graph by means of a graph database and a graph neural network. And finally, realizing three-dimensional visual display of the cultural resources by combining a virtual reality technology. According to the method, the problems of non-abandoned multi-modal data processing splitting, insufficient semantic organization and weak display interactivity in the prior art are solved, and the digital governance level and the propagation capability of cultural resources are improved.

The invention provides a method and equipment for constructing a text entity recognition model based on large model data enhancement. The method comprises the following steps: firstly, constructing an initial model comprising a preprocessing unit, a syntax dependency analysis unit, a data correction enhancement unit, a context coding unit, a syntax enhancement unit, an expression fusion unit and a sequence decoding processing unit; preprocessing the training sample to obtain a preprocessed text, and establishing a preliminary dependency graph through syntactic analysis; correcting and enhancing the text and the dependency graph by the large language model to obtain a text sequence and a dependency graph for subsequent use; encoding the text sequence to obtain an initial representation vector, and enhancing the vector in combination with the dependency graph; after fusion, decoding and outputting a prediction label containing a lexical entity label; and calculating loss by using a function containing conditional random field structure loss, judging convergence, and if not, updating parameters and continuing training until a target model is obtained. According to the method, data are optimized and expanded by means of a large language model, syntactic dependency enhancement features are combined, and the recognition capability of the complex text named entities is improved.