280 results about "Language understanding" patented technology

A real-time system for the multimodal generation of virtual reality scenes based on artificial intelligence for the creation of immersive three-dimensional environments from natural language narratives, consisting of: a speech capture module configured to continuously record a user's spoken narrative via one or more directional microphones, preprocesses the captured signal by noise reduction and temporal alignment, and outputs a digital speech stream; A speech-to-text processing unit that is operationally coupled to the speech capture module and configured for real-time speech recognition using a continuous neural transformer model. The unit is trained to transcribe natural language utterances into structured text data while maintaining contextual continuity throughout the evolving narrative. a semantic interpretation processing unit that is communicatively linked to the speech recognition unit and configured to perform natural language understanding techniques to extract contextual entities, spatial references, temporal relationships, and object attributes from the transcribed narrative; the engine includes a large language model that is fine-tuned for spatial reasoning tasks; a scene graph generation module configured to transform the interpreted semantic data into a structured, hierarchical representation that defines nodes for identified entities and edges for corresponding relationships, with each node associated with metadata describing geometry, position, orientation, texture, and linking attributes between objects; a multimodal image-language model processor coupled with the scene graph generation module, wherein the processor is configured to retrieve, adapt, or synthesize appropriate three-dimensional elements from a pre-trained visual-lexical embedding space and align these elements with their semantic and spatial definitions derived from the scene graph; a scene assembly and rendering controller configured to create a cohesive virtual scene from the aligned assets, perform real-time rendering using a GPU-accelerated ray tracing pipeline, and produce a stereoscopic visual output that corresponds to the evolving narrative; A head-mounted virtual reality visualization device connected to the rendering engine and configured to display the generated immersive environment to the user in real time. The device features motion sensors and inside-out tracking cameras to detect head and body movements, dynamically updating viewing angles and perspective within the rendered scene; and a bidirectional feedback module integrated into the head-mounted device and connected to the semantic interpretation processing unit; the module is configured to interpret corrective commands, gestures, or supplementary comments from the user to refine or modify specific scene elements without interrupting the real-time visualization; The system continuously updates the virtual scene as the narrative develops, ensuring temporal synchronization between speech input and rendered output below a defined latency threshold, thus enabling a natural, dialogic construction of complex three-dimensional virtual environments.

The invention discloses a double-engine government affair question and answer method based on large model fine tuning and RAG retrieval, belongs to the field of government affair digitization and natural language processing, and combines large model language understanding generation ability, retrieval enhancement generation technology and a structured reasoning mode. The defects of a traditional government affair question and answer method in the aspects of dynamic policy response, complex semantic understanding and compliance control are overcome. Government affair field knowledge is adapted through large-model fine adjustment, and high-precision and timeliness answering of government affair consultation is realized in combination with vector retrieval and a dynamic updating mechanism. The core innovation of the method lies in deep fusion of a double-engine architecture and dynamic knowledge management, the accuracy and response efficiency of government affair questions and answers are improved on the premise of ensuring policy compliance, and the method is suitable for intelligent upgrading of scenes such as government affair service halls and online consultation platforms.

The invention relates to the technical field of artificial intelligence and Internet of Things, and discloses an AI interaction intelligent module based on a hybrid architecture, comprising a user interaction unit which supports voice, text and image multi-modal input and integrates intention recognition and context understanding algorithms; the data processing unit is used for carrying out structured processing on the electric appliance specification and the historical fault data and constructing a dynamically updated knowledge graph; the hybrid architecture core unit comprises a deep learning subunit for realizing natural language understanding and generation based on a Transform model, and a knowledge reasoning subunit; a fault diagnosis unit; and a feedback optimization unit. According to the method, seamless cooperation of deep learning and symbol logic is realized through a dynamic routing strategy, a high-confidence-coefficient scene generates a response through a Transform model, a medium-confidence-coefficient scene calls a knowledge graph rule for verification, and a low-confidence-coefficient scene supplements information through multiple rounds of interaction, so that the effect of improving balance efficiency and safety is achieved.

The invention discloses a multi-modal natural language understanding and generating system and method. The method comprises the following steps: constructing a cross-modal pre-training module, training a multi-modal encoder, and establishing a cross-modal association mapping space; mixing prompt fine tuning is carried out, and a complete blank filling template is constructed; according to the intention reasoning network, extracting multi-round dialogue intention representation of the user, and retrieving an external knowledge base for fine-grained reasoning; constructing a unified semantic representation framework, embedding the text, the image and the voice into a unified space, and generating a query vector of multi-modal intention perception; and the knowledge query module based on key value memory generates entity-level multi-modal replies and optimizes the semantic comprehension and generation capability of the dialogue model. According to the method, the multi-modal information understanding and generating capacity is improved, deep association and understanding of image and text information are achieved, downstream task adaptability is enhanced, task completion accuracy and efficiency are improved, unified semantic representation of the multi-modal information is achieved, and support is provided for information retrieval and utilization.

A computer-implemented method for operating an artificial intelligence voice agent system includes receiving voice input through communication channels; analyzing converted text through natural language processing (NLP) pipelines implementing intent recognition and sentiment analysis detecting emotional cues using a multimodal large language model (LLM); generating response content using machine learning models trained on domain-specific corpora; converting generated responses to synthetic speech through text-to-speech (TTS) engines; integrating with a customer relationship management (CRM) platforms or an enterprise resource planning (ERP) database; and implementing continuous learning by updating language understanding models using conversation logs, voice recognition parameters based on user feedback, and response generation patterns. One implementation is a computer-implemented system and method that operates a suite of intelligent interactive devices and platforms including an artificial intelligence voice agent, enhanced communication platforms, an intimacy companion system, and augmented / virtual reality eyeglasses. Further, one implementation includes AR / VR eyeglasses that project visual content onto interchangeable lenses or directly onto the user's retina via laser-based retinal projection, provide prescription adjustments, incorporate ear-mounted sensors for monitoring physiological parameters like heart rate, oxygen saturation, and blood pressure, and utilize wireless data transmission, onboard environmental sensing, and remote calibration, all designed to offer dynamically adaptive, secure, and context-aware interactions across communication, personal assistance, health monitoring, and immersive augmented or virtual reality environments.

The invention provides an AI-assisted large-model intelligent question and answer accuracy improvement method, and belongs to the technical field of intelligent question and answer. Comprising the steps of data collection, data cleaning, data labeling, dynamic knowledge base construction, knowledge base updating trigger mechanism establishment, pre-training language model improvement, post-processing language understanding enhancement, combination of rule-based and machine learning reasoning, reasoning result verification and optimization, dialogue strategy optimization, emotion perception and response, multi-language search and language recognition search. And multi-dimensional evaluation index setting and regular evaluation and feedback improvement are realized. The intelligent question and answer accuracy improving method is high in understanding ability, timely in data updating, outstanding in reasoning ability and high in language recognition and dialogue ability.

Aiming at the problems of insufficient language understanding depth, weak personalized dialogue generation ability, lack of continuous learning and long-term user state modeling and the like in the current emotion recognition and psychological intervention technology, the invention provides a user emotion recognition and psychological intervention method combined with a large language model (LLM). According to the method, the potential emotional state is identified by analyzing free text information input by a user by utilizing the powerful capabilities of a large language model in the aspects of natural language understanding, emotional modeling and text generation; constructing a multi-round dialogue context, and reasoning a psychological change trend of the user; in combination with a psychological knowledge base, personalized and mild psychological intervention dialogue content with a dredging effect is generated. The system supports recognition and classification of various emotional states such as depression, anxiety and alonity, is suitable for various interaction scenes (such as APPs, webpages and social robots), and can greatly improve the precision of emotion recognition and the timeliness and effectiveness of psychological intervention. The emotion recognition and psychological intervention method based on the large language model provides solid technical support for constructing an intelligent, continuous and personalized psychological health management system, and has wide application prospects and profound social significance.

The invention discloses a knowledge graph construction method and apparatus, and a medium. The method comprises the steps of collecting a to-be-processed text for constructing a knowledge graph in a specified field; screening out a target text from the to-be-processed text, wherein the correlation between the target text and the specified field meets a preset condition; processing the target text through a pre-constructed target large language model to construct an initial triple; filtering the initial triple to obtain a target triple; and constructing the knowledge graph through the target triple. Therefore, through strict correlation screening conditions, texts irrelevant to the specified field are removed, the influence of noise data on knowledge graph construction is reduced, and the quality and accuracy of the knowledge graph are improved. On the basis of the target texts, the knowledge triples are efficiently and accurately extracted from a large number of target texts by means of the powerful language understanding and generating capacity of the large language model, the labor cost overhead of manual extraction is reduced, and the knowledge graph construction efficiency is improved.

The invention provides a method and device for scoring and questioning according to interview question answers and electronic equipment, and belongs to the technical field of artificial intelligence, in the method, multiple similarity measurement algorithms are integrated to achieve multi-angle quantitative evaluation of applicant answers, a large model scoring link based on deep learning is creatively added, and the evaluation efficiency is improved. Therefore, the language understanding depth which cannot be reached by pure mathematical calculation is compensated. According to the double-track scoring architecture, on one hand, the objectivity and the consistency of scoring are guaranteed by means of an accurate mathematical model; on the other hand, subjective understanding and interpretation of scoring are enhanced by means of a large model with high semantic analysis ability, the accuracy and comprehensiveness of evaluation are improved, the process is automatically achieved, efficiency is high, and the method can rapidly, accurately and comprehensively evaluate the applicant answers. In addition, the scoring reason and the question-asking problem given by the large model provide valuable information for mining the knowledge mastering condition of the applicant.

Described herein is an Autonomous Conversational AI system, which does not require any human configuration or annotation, and is used to have multi-turn dialogs with a user. A typical Conversational AI system consists of three main models: Natural Language Understanding (NLU), Dialog Manager (DM) and Natural Language Generation (NLG), which requires human provided data and configuration. The system proposed herein leverages novel Conversational AI methods which automatically generates conversational AI configuration from any historical conversation logs. The automatically generated configuration contains Auto-Topics, Auto-Subtopics, Auto-Intents, Auto-Responses and Auto-Flows which are used to automatically train NLU, DM and NLG models. Once these models are trained for given conversation logs, the system can be used to have dialog with any user.

The invention relates to the technical field of electric digital data processing, in particular to an artificial intelligence algorithm-driven digital human language understanding and corresponding reaction interaction method. The method comprises the following steps: collecting historical language data, text data, action instruction data and physical environment information of a user to obtain structured training data; vectorizing the text data to obtain a text semantic vector; environment feature vectors are extracted from the physical environment information; splicing the text semantic vector and the environment feature vector to obtain an environment enhanced text vector; and constructing a pre-training language model based on the environment enhanced text vector, and performing secondary pre-training to generate an optimized language model parameter. According to the invention, by fusing the user language and the physical environment information and combining with the multi-module intelligent component, context perception, efficient understanding and multi-modal natural interaction of the digital human in a complex scene are realized, and the intelligence, adaptability and user experience of the system are remarkably improved.

The invention discloses a method and device for achieving intelligent AI webpage content obtaining based on LLM, a storage medium and a program product, and relates to the technical field of data processing. According to the method, the search instruction input by the user is received, the search instruction is input into the trained semantic analysis model, after the semantic analysis model converts the search instruction into the query parameter, the query parameter is sent to the search engine, then the webpage obtained by the search engine according to the query parameter is received, and the search result is obtained. The method comprises the following steps: acquiring a webpage, determining a target webpage from the webpage according to a preset screening rule, crawling an HTML (Hypertext Markup Language) source code of the target webpage based on a pre-acquired authorization result, inputting the HTML source code into a trained LLM model, and determining a target code label in the LLM model according to the HTML source code to obtain text content in the target code label. Wherein the LLM model has a strong language understanding capability and can dynamically adapt to the change of a webpage structure, so that the time cost of crawling webpage contents is reduced.

In one embodiment, a method includes receiving training utterances associated with a domain, receiving ontology labels for the domain, wherein the ontology labels comprise one or more of an intent or a slot, generating an inventory for the domain, wherein the inventory comprises at least a respective index and respective span for each intent or slot, wherein the respective span comprises a respective descriptive label associated with the intent or slot, and wherein the respective descriptive label comprises a natural-language description of the intent or slot, generating frames for training utterances based on the training utterances and the inventory by a natural-language understanding (NLU) model, wherein each frame comprises a structural representation of the respective training utterance, wherein the structural representation is generated based on a comparison between the corresponding training utterance and the inventory, and updating the NLU model based on the frames.

The invention belongs to the technical field of electric power, and relates to an electric power system language understanding pre-training method based on BERT and reinforcement learning, which provides a professional corpus basis for a model by collecting and preprocessing data in the field of electric power; power proprietary vocabularies are added into the BERT model and fine tuning is carried out, so that the understanding ability of the model to domain terms is improved; a power system knowledge graph is constructed and embedded into the model, and domain knowledge representation of the model is enhanced; by defining a task environment and applying reinforcement learning, optimization of the model on a specific power task is realized; according to the method, limited field resources are fully utilized, the advantages of the pre-training model, the knowledge graph and reinforcement learning are combined, the language understanding ability of the BERT model in the professional field of the power system is effectively improved, and a new thought is provided for solving the problem of natural language processing in the professional field.

The invention discloses an automatic data modeling and optimizing system and method fusing a knowledge graph and ChatBI, and relates to the technical field of data analysis. In order to solve the problems of high interaction threshold and insufficient analysis depth of a traditional BI tool, the scheme adopted by the invention comprises a data layer which has the capabilities of multi-source data access, domain knowledge graph construction, intelligent mapping recommendation, federal calculation and data mild governance, and realizes data integration and semantic unification; the analysis layer realizes accurate conversion from a natural language to an SQL and semantic reasoning of a knowledge graph through graph vectorization, multi-model cooperation, natural language understanding, intelligent SQL generation and graph dynamic updating, and supports efficient data query and analysis; and the application layer has the functions of natural language interaction, visual recommendation and generation, root cause analysis and intelligent early warning, and provides a visual interaction interface and data display service for a user. According to the invention, intelligent data analysis and visualization based on natural language interaction can be realized.

The invention provides a power industry-oriented language understanding and dialogue generation method, which comprises the following steps of: retrieving a dialogue process template related to a current problem from a pre-constructed power industry multi-round dialogue knowledge base according to a field label of the problem, and determining a basic framework of a dialogue process and key information required to be acquired by each round of dialogue; after each round of dialogue is finished, the obtained information is synthesized, reasoning is carried out through a pre-constructed electric power knowledge graph, the incidence relation between different professional fields is analyzed, key points and possible solutions of questions are deduced, and candidate answers are generated; and establishing an answer sorting model by applying deep learning, scoring and sorting the candidate answers, and selecting the answer with the highest score as the reply of the current round in combination with the correlation, integrity and specialty of the answers.

The invention discloses a sensitive information recognition method and device, equipment, a storage medium and a program product, and relates to the technical field of information recognizing.The method comprises the steps that a to-be-detected text is preliminarily screened, and a sensitive marked text and an unmarked text are obtained; retrieving the unmarked text based on the vector database to obtain a plurality of candidate sensitive data, and fusing the candidate sensitive data with the to-be-detected text to obtain a fused text; performing context analysis on the fused text through a large language model to obtain sensitive confidence data; and generating a sensitive information identification report according to the sensitive mark text and the sensitive confidence data. According to the method, the vector database is fused for retrieval, so that the conditions of false report, missing report, misjudgment and the like are avoided; and a large language model with advanced language understanding ability is combined for analysis to obtain sensitive confidence data, so that sensitive information in a natural language can be accurately identified and processed. Through layer-by-layer identification of the vector database and the large language model, the accuracy of sensitive information identification is improved.

The invention belongs to the technical field of case information extraction, and particularly relates to a case information extraction system based on an AI large model, which comprises an input module, a processor, an AI screening model, a case database and a display, the processor is connected with the AI screening model, the processor converts input case information into an instruction recognized by the AI screening model, the AI screening model is connected with the case database, and the AI screening model carries out case screening in the case database based on the instruction. Cases of a patient are stored in a case database, the cases in the case database are browsed and screened based on an AI screening model, medical record texts are pre-trained based on a large-scale pre-training language model, then fine adjustment is carried out on a specific medical record data set, and by means of the powerful language understanding ability of the model, the medical record data set can be obtained. And the accuracy and recall rate of named entity recognition are improved.

The invention relates to a pre-training language model fine tuning method, an inference service system, equipment and a medium, and the method comprises the steps: obtaining an original instruction of a target field and related fields, the original instruction comprising a language understanding task; evaluating and screening the original instruction according to a multi-dimensional index, and constructing a first high-value instruction set; extracting instructions in related fields from the first high-value instruction set, and performing filtering and mobility evaluation on the extracted instructions by using a pre-training language model to obtain a second high-value instruction set; adopting a pre-training language model to perform labeling, screening and enhancement processing on the second high-value instruction set in sequence, and generating a structured training sample; and training a pre-training language model according to the structured training sample to enable the pre-training language model to adapt to the target domain. Through the method and the device, the field annotation data quantity is reduced.

The invention discloses a document auditing method and device based on cooperation of a large model and a rule engine. The method comprises the following steps: analyzing an original document to form an intermediate representation file; extracting entities from the intermediate representation file to form an entity candidate set; inputting the thinking chain cue word into the large model to obtain a first triple, a first confidence coefficient and a first risk level, and calculating a first traceable score of the large model according to the reasoning path node; performing deterministic judgment by using the rule engine to obtain a second triple, a second confidence coefficient, a risk level and a second traceable score; calculating two weighted voting values and obtaining a conflict difference value; taking a conclusion corresponding to the high-weighted voting value as a final conclusion when the conflict difference value is smaller than a preset judgment threshold value; otherwise, starting an artificial rechecking process; and generating audit reports in various formats. According to the method, the advantages of relatively high language understanding ability of a large model and relatively high certainty of a rule engine are exerted, and the problem of missed checking or excessive marking is avoided.

The invention relates to the technical field of large language models, in particular to a natural language question and answer framework, method and device based on self-reflection, and can solve the problem that large language models LLMs (Language Models) such as ChatGPT and PaLM in the prior art show excellent performance in various language understanding and generation tasks to a certain extent, and the problem that the Language Models are difficult to understand and generate can be solved to a certain extent. However, the ability of the method in the aspects of complex reasoning and complex knowledge utilization is still lower than the human level. The framework comprises: a thinking chain module for receiving an input question, outputting a model thinking process according to the input question, and obtaining and outputting an answer at the end; the persuaser module firstly evaluates the correctness of the answer and the reasoning step, and outputs a corrected reasoning path to the responder module if an error exists in the reasoning step; and the answerer module is used for providing answers according to the corrected reasoning path and self-prompted question type information, and optimizing the output accuracy of the large model through repeated iteration.

The invention provides an intelligent customer service system, which comprises an information acquisition module used for identifying a customer identity according to a user login account and obtaining historical data under the account; the grading module is used for establishing a customer portrait model according to the historical data and classifying customer types; the matching module is used for automatically matching a service strategy model version corresponding to the account according to the customer type obtained by the grading module; the knowledge graph response module is used for carrying out deep analysis on client input contents based on the natural language understanding capability of a large language model in combination with a context, a client portrait and historical data after the version of the service strategy model is determined, automatically generating a task list and a semantic tag tree for a dialogue, and continuously optimizing the task list and the semantic tag tree; starting from a problem node of the task list, analyzing a corresponding semantic tag along an optimized and trained path in the knowledge graph, and matching a service processing action; and triggering a back-end system interface associated with the node according to needs, transferring real data, and solving client problems.

The invention discloses a 3D point cloud large model dialogue security protection system and method based on reinforcement learning and a protection layer, a reinforcement learning security alignment module takes a GPT4Point framework as a base, constructs a point cloud-language joint embedding space, extracts global semantic features by using a multi-layer Transform architecture, realizes efficient alignment of 3D point cloud and text instructions, and improves the security of the 3D point cloud large model dialogue. According to the multi-modal protection layer architecture, lightweight Lama Guard serves as a core model, and cross-modal risk interception is achieved by analyzing geometric features of text entities and associated point clouds generated by a target model in real time. According to the invention, through fusion of reinforcement learning security alignment, a large model protection layer and a GPT4Point multi-mode framework, a multi-level security protection system oriented to a 3D point cloud large model dialogue system is constructed. According to the collaborative design of a reinforcement learning dynamic optimization strategy and a Lama Guard filtering mechanism, the robustness of the model under attack resistance and the adaptability of the model in a natural distortion scene are remarkably improved, and the unified architecture of GPT4Point provides efficient support for point cloud-language understanding and generation.

The invention discloses a data security classification and grading method and system based on a large language model, and relates to the technical field of language analysis, and the method comprises the steps: converting bank internal business data of each layer into text data through text conversion, unifying the data form of multi-layer or multi-modal data into text data, and storing the text data in a database; and a reliable basis is provided for subsequent language semantic recognition. According to the method, the text data is partitioned and sorted, subsequent context language understanding is facilitated, the context window length under each service scene is set, the context semantic understanding requirement of each service scene is met, and the reliability and accuracy of semantic association are improved. The internal business data of the bank is classified and graded based on the risk value of the business scene and the sensitive value of the composite field, the accuracy and adaptability of data security classification and grading are improved, security protection strategies of different degrees are carried out for different levels of different types of data, and the requirements of bank data storage and prevention are met.

Disclosed herein are computer-implemented methods of providing universal language understanding, the computer-implemented method including parsing an input in a language domain into an enriched tree data-structure, wherein the enriched tree data-structure comprises at least one of a unique identification of an entity, a classification, and a resolution; processing the enriched tree data-structure by matching the enriched tree data-structure with one or more dynamic language graphs, wherein the one or more dynamic language graphs store a relation between elements of the language domain, a causation of the entities, and a property of the entities; and upon determining the enriched tree data-structure does not match with the one or more dynamic language graphs, at least a portion of the enriched tree data-structure that does not match is inserted into the one or more dynamic language graphs.

The invention discloses a medical training evaluation method and system based on an intelligent simulation patient, and relates to the field of artificial intelligence technology and medical simulation, and the method comprises a virtual patient model construction step, a multi-modal perception step, an intelligent interaction step and an intelligent evaluation step. By integrating voice recognition, natural language understanding, facial expression recognition, gesture recognition and virtual reality technologies, a high-simulation and high-interactivity intelligent patient model is constructed for medical students or clinical medical staff to perform diagnosis and treatment training and skill operation assessment. The virtual patient model supports custom information such as age, gender, race, medical history, living habits and the like, different virtual patient images and different pathophysiological states are generated, and personalized learning requirements of users are met. In addition, targeted training suggestions are provided, the training difficulty and content can be adjusted according to the performance of the user, and different training requirements are met.

System and method for using knowledge fabric based on knowledge ontology, designed for deep language understanding and mechanistic causal reasoning, and meta-knowledge repository for auditable question answering. The method includes receiving an input text from a user, building a knowledge graph that represents real world facts and associations in the form of contextually tagged and weighted knowledge propositions, in multiple knowledge domains. The knowledge graph in combination with causal path knowledge and metadata describing digital sources containing answers constitutes the knowledge fabric. The method includes resolving ambiguity and determining actual intent of the user for the input text, from a plurality of interpretations of intent for sentences using the knowledge graph in conjunction with logical inference to achieve deep natural language understanding. The method includes finding / delivering response to the input request as to why / how unknown factors resulted in known outcome, or what outcomes are likely given known causal factors.

The invention discloses an accompanying dialogue system and method based on real-time environment perception and knowledge graph enhancement. The system comprises a multi-mode real-time environment perception module, a knowledge graph construction and enhancement module, a natural language understanding and dialogue management module, a personalized recommendation and narration generation module and an immersive multi-mode interaction module. The method comprises the steps of multi-modal real-time environment perception and situation data generation; performing dynamic association, query and enhanced reasoning on the knowledge graph; natural language understanding and dialogue management, personalized recommendation and narrative generation, and immersive multi-modal interaction presentation and feedback reception. According to the method and the system, the concern point of the user, namely scenery or details, can be accurately positioned, and context information required by subsequent service intelligence is provided, so that the problems of poor environment perception ability, weak interaction immersion, dull knowledge service, lack of individuation, insufficient intelligent accompanying experience and the like in the existing tourism auxiliary technology are solved.

Embodiments described herein provide systems, methods, and devices for generating enhanced vison-language training data. A method may include: receiving, from a communication interface, a first training dataset of image-text pairs and a second training dataset of annotated image-text pairs; fine-tuning an image-grounded text decoder and an image-grounded text encoder using the second training dataset of annotated image-text pairs; generating, by the fine-tuned image-grounded text decoder, a predicted text based on a training image from the first training dataset; generating, by the fine-tuned image-grounded text encoder, a filtering decision based on the training image and the predicted text; adding the training image and the predicted text to form a third training dataset of image-text pairs depending on the filter decision; and training a vision-language model using the third training dataset of image-text pairs.

The invention belongs to the technical field of contract review, and particularly relates to an intelligent contract review analysis method and system based on a large language model. According to the method, dynamic generation of rules and a neural symbol reasoning mechanism are mainly fused, and the core is to realize automatic and precise risk identification and evaluation of contracts by utilizing the powerful language understanding and generation capability of a large language model (LLM); the method comprises the specific steps of generating a contract review rule, extracting facts from a contract to be reviewed, performing review reasoning and generating a review report. According to the method, the inherent logic preciseness of symbol deduction and the powerful semantic understanding capability of a large language model are organically combined, so that core elements and potential risks in a contract can be more accurately identified, and contract terms which are complex in expression or have hidden agreement can be effectively processed and expressed.