898 results about "Similarity computation" patented technology

The invention discloses an intelligent class case retrieval method and system based on reinforcement learning self-feedback, and the method comprises the following steps: S1, constructing a multi-level semantic understanding framework, carrying out the semantic understanding of a query case through the multi-level semantic understanding framework, and generating a complete semantic representation; s2, carrying out multi-dimensional similarity calculation and sorting optimization on the query case and historical discriminants in the candidate case library; s3, constructing a dynamic user portrait and optimizing a recommendation strategy; and S4, establishing multiple rounds of dialogues and context awareness, and carrying out visual display and feedback mechanism optimization. According to the intelligent class case retrieval method and class case retrieval system based on reinforcement learning self-feedback, a more accurate, efficient and reliable law intelligent retrieval system is finally achieved, judicial practice requirements are met, and quantitative and measurable technical progress is achieved in the aspects of retrieval accuracy, sorting quality, personalized service, law adaptability and the like.

The invention provides a bidirectional visual semantic interaction enhancement method and system for generalized zero sample learning, and the method comprises the steps: collecting visual samples containing visible classes and unvisible classes and corresponding cross-class semantic descriptions in a generalized zero sample learning scene; respectively generating a global visual feature vector and a semantic word vector through a visual feature extractor and a semantic encoder; fusing the two by using a multi-head attention mechanism to generate a semantic enhanced visual embedding representation; then visual-to-semantic and semantic-to-visual two-way comparative learning with expert attribute vectors as alignment targets and starting points is carried out, and visual semantic embedding and attribute vector alignment are promoted in combination with attribute regression loss; then training a bidirectional visual semantic interaction enhancement model based on multi-loss joint optimization; and finally, calculating visual semantic embedding for a test image by utilizing the trained model, and outputting a classification result of a visible class or a non-visible class by combining similarity calculation with a candidate class attribute vector with a self-calibration item.

The invention discloses a private network content copyright monitoring and evidence obtaining system based on AI and a large model, which utilizes AI and large model technologies to carry out copyright monitoring and evidence obtaining on multimedia content in a private network environment, and carries out deep semantic understanding and cross-modal feature extraction through a large model processor to generate unified semantic representation. The method comprises the steps that a copyright content database is established and stored in a copyright content knowledge base, the copyright content knowledge base is used for storing metadata of original content protected by copyright, unified semantic representation and copyright declarations in a natural language form provided by a copyright party, and the copyright declarations are converted into query vectors through a semantic understanding technology; the method comprises the following steps: acquiring a semantic representation of a multimedia content, performing similarity calculation with the semantic representation of the multimedia content, identifying infringement content, when the infringement content is identified, recording an original source, publishing time and publisher information of the infringement content, performing differentiation analysis, generating an evidence chain, displaying a copyright monitoring result through a user interface, generating infringement alarm information, and presenting details of the evidence chain.

The present invention relates to the technical field of intelligent out-of-distribution fault detection for construction machinery, and discloses an out-of-distribution fault detection method and system based on energy propagation and graph learning, and the method includes: acquiring vibration acceleration signals in typical fault states, carrying out similarity calculation to obtain an adjacency matrix composed of the maximum mutual information coefficients, and taking the adjacency matrix as input in a graph neural network; carrying out feature extraction on the adjacency matrix through adopting a GraphSage graph convolution method, and generating each node representation; calculating an energy score of each node, and distinguishing between in-distribution data and out-of-distribution data; and enhancing out-of-distribution data confidence estimation for each node, and carrying out out-of-distribution data identification and out-of-distribution data detection under different working conditions of a rolling bearing.

The invention discloses a large model calling system and method based on an MCP tool chain, and the method comprises the steps: receiving user input, and carrying out the preliminary matching of tools: querying whether the user input contains an entity in an entity database or not through a fuzzy matching technology, and carrying out the preliminary screening of a plurality of candidate tools based on a matching result, so as to obtain limited to-be-selected tools; user input is converted into semantic vectors, semantic matching is conducted in a vector database, and semantic similarities of all tools to be selected are ranked through a similarity calculation method; extracting key information in user input; abstracting the user input into a specified format; and matching the specified format with the historical workflow template, and if the historical workflow template is matched, replacing the key information to a corresponding position of the historical workflow template by adopting a structure-preserving parameter replacement algorithm to realize workflow reuse. According to the method and the device, a complete technical link from accurate tool screening to intelligent workflow multiplexing can be realized.

The invention belongs to the computer vision technology, and particularly relates to a small sample multi-modal prototype learning-oriented remote sensing scene classification method, which comprises the following steps of: acquiring RGB (Red, Green and Blue) images with category labels and text prompts of the RGB images as a support set; establishing a text prototype, an RGB prototype and a hyperspectral prototype of each category according to the support set; and extracting to-be-classified query set image features by using a pre-trained CLIP image encoder, calculating cosine similarities between the query set image features and the text prototype, the RGB prototype and the hyperspectral prototype of each category of the support set, taking the cosine similarities as input of a multi-layer perceptron, and obtaining the category of the to-be-classified RGB image through classification of the multi-layer perceptron. High-precision and high-robustness remote sensing scene classification is realized under the small sample condition, only prototype and similarity calculation is needed in the reasoning stage, and deployment and expansion are easy.

The embodiment of the invention provides a knowledge graph fusion method and system based on a large model, and the method comprises the steps: carrying out the data standardization, entity feature enhancement and relation semantic annotation of heterogeneous knowledge graphs from different sources; through semantic similarity calculation, context reasoning and alignment confidence evaluation, a multi-stage and multi-mode entity alignment mechanism is constructed in combination with a large language model, and cross-source entity matching is performed on entities in heterogeneous knowledge maps of different sources; based on conflict detection, dynamic weight distribution and a conflict resolution strategy, generating a data fusion result, and unifying multi-source data; and generating a high-quality unified knowledge graph through missing relationship prediction, logic consistency verification and an incremental updating mechanism. According to the knowledge graph fusion method and device, full-process automation, precision and dynamics of knowledge graph fusion are realized, the problems of high manual dependence, weak semantic processing capability, poor cross-domain adaptability and the like in the prior art are effectively solved, and the efficiency and quality of knowledge graph fusion are remarkably improved.

The invention relates to an electricity market price difference prediction method and system based on dynamic similar day matching. The method comprises the steps of obtaining electricity transaction data; processing the power transaction data to obtain a bidding space index and a price difference direction index; calculating the similarity between the day-ahead electric power spot price data and the real-time electric power spot price data; calculating a comprehensive similarity index according to the similarity; screening out data of the comprehensive similarity index in a set range to form a similar day set, and training a price difference classification prediction model by using the similar day set; and predicting the spot price difference direction of a prediction day by using the price difference classification prediction model. According to the method, training data samples are screened through a similarity algorithm, the over-fitting risk caused by invalid data volume is reduced, then the influence of extreme prediction of an individual algorithm is reduced through model diversity, and finally a robust probabilistic price difference direction prediction result is output.

The invention relates to the technical field of passenger service, and discloses a construction method and device of a dynamic extension word bank, equipment and a medium. The method comprises the following steps: acquiring multi-source heterogeneous original knowledge data related to civil aviation passenger service, and converting the multi-source heterogeneous original knowledge data into a standard format text; processing the unstructured data in the standard format text based on the adaptive word segmentation model and the stop word list to obtain a plurality of candidate words; performing multi-dimensional corpus feature quantitative analysis on each candidate word to determine candidate keywords; and performing similarity calculation based on the entity word segmentation and the candidate keywords, determining effective candidate keywords, and adding the effective candidate keywords into the dynamic expansion word bank. By means of the method and device, the technical problems that in the prior art, a word bank used by a civil aviation service system is usually based on a static vocabulary or depends on manual compiling and updating, the static word bank cannot be updated in real time, the characteristics of the civil aviation field cannot be flexibly handled, and the maintenance cost is high due to manual updating and maintenance are solved.

The invention relates to the technical field of intelligent matching, and discloses a big language model dynamic position recommendation method and system based on dialogue contexts.The method comprises the steps that candidate skill label analysis is conducted on natural language input of candidates, a semantic analysis result is generated, and based on the semantic analysis result, a big language model dynamic position is recommended; dynamically updating the candidate portrait, carrying out semantic matching on candidate portrait data and standardized post information to obtain a post matching result, carrying out post similarity calculation on the post matching result, generating an initial post recommendation list, then carrying out recommendation logic adjustment, generating feedback processing data, and carrying out post similarity calculation on the initial post recommendation list. Dynamically optimizing the initial position recommendation list in combination with the semantic analysis result, candidate portrait data and feedback processing data, generating position dynamic recommendation results, reordering the position dynamic recommendation results, and outputting a final position recommendation list; according to the method, the accuracy of dynamic position recommendation of the large language model based on the dialogue context can be improved.

The invention discloses a text travel knowledge association graph construction method and device, a storage medium and equipment. The method comprises the steps that firstly, target entities in a text travel knowledge text of a target area are extracted, and a matrix of the target entities and keywords is generated; then, according to the matrix of the target entities and the keywords, the association confidence degree between the two target entities in the target entity pair in the travel knowledge text is calculated; according to the association confidence coefficient and the semantic similarity between the two target entities in the target entity pair, calculating the semantic confidence coefficient between the two target entities in the target entity pair so as to generate an entity association rule base; using an entity association rule base to generate a candidate triple composed of a historical story entity, a non-abandoned skill entity and a derivative commodity entity; and a triple of which the association score is higher than a preset score threshold value is screened out to serve as a target triple, and the target triple is used for constructing a text travel knowledge association graph corresponding to the target region. Therefore, an ideal association effect is achieved.

The invention relates to the technical field of fault diagnosis, in particular to an intelligent street lamp fault diagnosis and maintenance system, which comprises a signal collaborative acquisition module, a frequency domain conversion module, a fault identification module, a coupling verification module and a maintenance scheduling module. According to the invention, through a multi-source data synchronous acquisition and time sequence alignment technology, the influence of external environment interference on monitoring data is eliminated, and the stability and accuracy of photoelectric signals are ensured. Stable signal features are extracted through frequency domain conversion and feature screening, accurate fault recognition is achieved based on frequency components and similarity calculation, multi-dimensional verification is conducted in combination with instantaneous power comparison and communication delay detection, the accuracy and reliability of fault judgment are improved, therefore, misjudgment and missed judgment are avoided, maintenance scheduling is optimized, and the fault diagnosis efficiency is improved. The fault response efficiency and the overall stability of the street lamp system are improved, maintenance lag and resource waste caused by inaccurate fault diagnosis are effectively reduced, and the operation reliability and the maintenance response speed of the street lamp are improved.

A computer implemented method matching an individual's continuous metabolite monitoring traces with another individual's continuous metabolite monitoring traces begins by identifying a concentration level of a metabolite for a subject. Dividing metabolic states into ranges of concentration levels allows for transforming a concentration level into a symbol from a set of symbols, wherein the symbol represents a category of concentration levels. The method identifies one or more strings of symbols, wherein each string of symbols represents a series of one or more symbols corresponding to a series of transformed concentration levels represented by the symbols. A computer is programmed to determine a lexical distance between a first string from the one or more strings of symbols and a second string, wherein the lexical distance corresponds to a degree of similarity between the first string and the second string. The computer provides a search result for a search query based on the determined lexical distance.

The invention discloses a method and a system for automatically generating a video based on multi-agent unstructured knowledge, and relates to the technical field of video automatic generation based on knowledge processing, and through a data source weight evaluation mechanism and a content semantic similarity calculation method, repeated or contradictory knowledge fragments are automatically identified, a disputed knowledge unit list is established, and the video is automatically generated according to the disputed knowledge unit list. And analyzing the matching degree of the dispute content and the preset script by adopting a semantic vector similarity algorithm. And for the recognized dispute knowledge points, a standby knowledge replacement algorithm is applied to retrieve replacement contents with high confidence from a reliable knowledge base, a knowledge unit replacement operation is executed, and high-quality video output with dispute identification is generated, so that the content accuracy and credibility of the multi-source knowledge fusion video are effectively improved. According to the method, a whole-process quality control system from knowledge identification to video generation is established, intelligent matching of knowledge credibility evaluation and a video content presentation mode is realized, and practical deployment of the technology is restricted.

The invention discloses an industrial operation and maintenance high-risk instruction intelligent identification method and system based on instruction sequence analysis, and the method comprises the steps: intercepting an operation and maintenance instruction inputted by a remote user in real time through an operation and maintenance gateway, and constructing an instruction sequence according to a time window; performing vectorization representation on the instruction sequence, and generating an overall sequence representation vector by adopting a sequence coding model; performing similarity calculation on the overall sequence representation vector and the high-risk sequence center vector, and outputting risk confidence; calculating a comprehensive risk score in combination with the permission coefficient, the time sensitivity coefficient and the context factor; and making a decision according to a comparison result of the comprehensive risk score and a risk threshold, and identifying an industrial operation and maintenance high-risk instruction. The method can accurately identify the potential high-risk instruction behavior, reduces the system security risk, and has the advantages of high identification precision, strong real-time performance and good expandability.

The invention discloses a medical knowledge graph construction method and device for multi-source heterogeneous data fusion and incremental updating, and relates to the technical field of medical knowledge graphs. The method comprises the steps that multi-source medical data such as outpatient records, hospitalization medical records, inspection reports and image data of a patient in the whole life cycle are collected; performing time sequence alignment by adopting a space-time alignment algorithm to obtain standardized medical data, calculating a data fusion weight and constructing an incidence matrix; performing singular value decomposition on the incidence matrix to extract a feature vector, and obtaining a dynamic update coefficient through standardization, cosine similarity calculation, convolutional neural network processing and exponential weighted moving average; adjusting the reference map according to the dynamic updating coefficient increment to generate an updated medical knowledge map, and outputting diagnosis and treatment decision support information; and multi-dimensional medical data space monitoring abnormity can be constructed, a joint optimization model is established to generate a personalized diagnosis and treatment scheme, and edge deployment is realized through a knowledge distillation compression map.

According to the abnormal information monitoring and processing method based on the large model and the intelligent agent, system operation data and log information are collected in real time, abnormity is detected based on threshold rules, automatic discovery of abnormal events is achieved, and manual intervention is not needed; after the exception is detected, the exception event object is constructed and the context semantic analysis is performed, so that the exception category, the trigger module and the operation environment parameters can be accurately extracted, and the accurate positioning of the complex exception is realized. Cue words are generated based on the semantic enhancement model, and a scheme retrieval request is constructed, so that the retrieval process better conforms to semantic logic, and the accuracy and correlation of scheme matching are improved. Through semantic similarity calculation and an external knowledge source extension mechanism, the system can efficiently retrieve candidate solutions inside and outside a knowledge base, and determines an optimal solution based on credibility scores, so that the intelligence and reliability of decision processing are remarkably improved.

The invention discloses an unknown attack detection method and system for intelligent network security situation awareness, and the method comprises the steps: obtaining multi-dimensional data of a power grid monitoring system, carrying out the data preprocessing, carrying out the network attack preliminary detection of the multi-dimensional data through a pre-built network security situation awareness framework, and outputting suspected attack data, the method comprises the steps of performing data training on historical attack data through an improved OCN open set classification network, identifying attack features of each known attack type, performing semantic similarity calculation on the attack features and suspected attack data, performing attack feature mapping and clustering on the suspected attack data based on semantic similarity, and obtaining an unknown attack feature clustering result. And according to the unknown attack clustering result, carrying out attack type classification on unknown attacks in the suspected attack data to obtain an unknown attack detection result. The method has the effects of detecting unknown attack means in time, effectively reducing the risk that the power grid system suffers from network attacks and guaranteeing safe and stable operation of the power grid system.

The invention discloses a heat supply system optimization scheduling control method based on an operation benchmark library and working condition matching. The method comprises the following steps: constructing an offline operation benchmark library comprising a scheduling control business demand type, a business working condition type, business working condition relevance and a business operation strategy; business demand similarity and business working condition type similarity are calculated through the scheduling control agent; if the business demand similarity and the business working condition type similarity both exceed threshold values, business operation strategies under the business demand and the business working condition are obtained through an offline operation benchmark library, and operation strategies of other types of scheduling control businesses are adaptively adjusted according to business working condition relevance; taking the offline operation benchmark library as a training sample set through a scheduling control agent, carrying out training learning of each business scheduling control model, constructing each business scheduling control model, and obtaining a corresponding business operation strategy; and fusing and analyzing the benchmark strategy and the model strategy to obtain a final operation strategy of each scheduling control service.

The invention discloses a few-sample industrial processing anomaly detection method based on a pre-training model CLIP, and aims to solve detection pain points of scarcity of abnormal samples, high labeling cost and insufficient generalization in an industrial scene. The method comprises the following steps: data preprocessing: collecting an industrial image, dividing the industrial image into a support set only containing normal samples and a query set containing normal and abnormal samples, and combining data enhancement and local module clustering decomposition; cross-modal feature adaptation is carried out, text semantic anchor points are constructed through combination prompt integration, a residual adapter is inserted to optimize vision-text feature alignment, and multi-scale features are aggregated by adopting a harmonic average method; few-sample reference learning is carried out, a normal mode reference memory bank is constructed based on a prototype network, and feature learning is reinforced in combination with feature registration and alternate learning; and abnormal judgment: realizing abnormal recognition through cosine similarity calculation and a dynamic threshold value, and outputting an image-level confidence coefficient and a pixel-level segmentation image.

The embodiment of the invention relates to the technical field of intelligent text analysis, in particular to a demand information duplication judgment method and device based on a large model, equipment, a storage medium and a product, and the method comprises the steps: converting a demand text uploaded by a user into structured demand information through structured processing; screening similar historical demands from a historical demand library based on vectorization retrieval and semantic reordering; character-level and semantic-level two-dimensional similarity calculation is adopted, and a comprehensive score is obtained through weighted fusion; and automatically judging a demand repetition state according to a score threshold, and synchronously outputting an interpretability conclusion containing functional intention similarity description and field coincidence degree analysis. According to the scheme, through double verification of large model semantic understanding and structured rules, the accuracy and interpretability of demand duplicate judgment are remarkably improved, and meanwhile, continuous optimization and self-adaptive extension of a historical demand library are supported.

The invention provides a method and system for pre-training a sign language understanding framework based on semantic enhancement of skeletons, and relates to the technical field of sign language recognizing.The method comprises the steps that sign language video data and skeleton sequences and text data matched with the sign language video data are obtained; extracting a skeleton key point sequence from the sign language video data, modeling to form skeleton features, and performing word segmentation processing on a text; in a pre-training stage, skeleton features and word segmentation texts are input into a fusion network to generate bidirectional enhanced features, and global and local similarities are obtained through double-layer semantic alignment; calculating the comparison loss based on the similarity, and coordinating the weight through the balance parameter to obtain the hierarchical loss; executing the matching task and the language modeling task to obtain corresponding loss, weighting and combining the three types of loss into pre-training total loss, and adjusting parameters to complete training; and finally, part of parameters are optimized in combination with specific task types in a fine tuning stage, and enhanced understanding of sign language semantics is realized. The sign language understanding accuracy is improved.

The embodiment of the invention discloses an APT (Advanced Persistent Threat) network attack identification method, which comprises the following steps: collecting multi-source data from a plurality of data sources, and filtering current attack behavior data from the multi-source data; performing multi-dimensional similarity calculation on the current attack behavior data and the APT organization intelligence in the multi-modal threat knowledge graph to obtain a comprehensive similarity, the multi-modal threat knowledge graph being obtained by modeling after the multi-dimensional attack data and the threat intelligence are fused; nodes in the multi-modal threat knowledge graph are used for representing APT organizations, TTP, used tools and attack targets, and edges connected with the nodes are used for representing relationships among entities represented by the nodes; and based on the comprehensive similarity and the multi-modal threat knowledge graph, performing attribution reasoning on the APT organization of the current attack behavior data to obtain the identity information of the attacker. According to the method, unknown threats can be identified, the identity information of an attacker can be obtained through accurate reasoning, and the utilization rate of intelligence is improved.

The invention provides a similarity retrieval system and method based on vector modulus length sorting. The system comprises a vectorization module, an index construction module, a self-adaptive candidate set generation module and a precise sorting module. Relates to the technical field, and the method comprises the following specific steps: S1, calculating a vector modulus length value of a database vector, generating a globally sorted ordered index according to the vector modulus length value, and constructing a density distribution diagram; s2, calculating the query modulus length of the query vector; s3, extracting a candidate vector set from the ordered index; s4, performing accurate similarity calculation on each candidate vector and the query vector, and outputting a retrieval result; according to the method, rapid coarse positioning is carried out by constructing a one-dimensional modular length ordered index, and the size of a candidate set is dynamically adjusted according to the local density of data by utilizing an adaptive modular length adjacent window calculation process, so that intelligent and adaptive regulation and control of a retrieval process are realized.

The present application belongs to the technical field of natural language processing for traditional Chinese medicine ancient books, and particularly relates to a method and system for constructing a variant character dictionary and text alignment of traditional Chinese medicine ancient books, and a medium. The present application combines the recognition of variant characters and the construction of a variant character dictionary to achieve a text alignment method for traditional Chinese medicine ancient books. Specifically, the present application uses deep learning and natural language processing technology to automatically extract variant character features, significantly improving the coverage range and recognition accuracy; through dynamic programming, semantic similarity calculation and knowledge graph fusion, the multi-modal features are comprehensively considered to significantly improve the alignment accuracy. At the same time, the model can dynamically adapt to new texts and variant characters, and has stronger expansibility and adaptability; and the knowledge graph is used to optimize the alignment result, improving the accuracy and efficiency of text processing. The final generated result is the aligned text sequence, in which the variant characters are correctly recognized and mapped to standard characters. The present application has good application prospects in the digitization of traditional Chinese medicine ancient books.

A system and method for adaptive ideation and innovation management using graph-based similarity computation and real-time social facilitation are disclosed, and the system may include a plurality of client devices operated by a human user and one or more AI Agents. The system may further include a computing platform running a Precompute Similarity Engine that may be configured to embed and index idea objects, precompute similarity clusters, and generate candidate merge or purge operations together with diversity and novelty signals; and running a Social Physics Engine that may be configured to monitor human user and machine participant (AI agent) interactions, compute group metrics, and issue digital facilitation interventions. The Precompute Similarity Engine and the Social Physics Engine may operate in an interdependent feedback loop such that diversity and novelty signals guide the generation of digital facilitation interventions that are communicated via the network to the plurality of client devices and the one or more AI Agents.

The embodiment of the invention discloses an operation link compliance automatic evaluation method and device based on a cross encoder. The method comprises the following steps: receiving multi-modal operation data and an unstructured evaluation standard document of an operation link; analyzing the semantic structure of the evaluation standard document by using a pre-training language model, and constructing a structured evaluation item set; the method comprises the following steps: performing content analysis on multi-modal operation data to construct an evidence library; based on similarity calculation, obtaining a target evidence set which is in semantic matching with the evaluation item; inputting the evaluation item and the corresponding target evidence set into a cross encoder, and obtaining a proving score of each target evidence to the evaluation item; and rendering and outputting a structured compliance evaluation report through a report generation engine based on the evaluation item, the proving score and the associated target evidence set. According to the method, each link for generating the compliance report can be converted into an automatic mode, so that the evidence retrieval efficiency is improved, and the compliance evaluation accuracy is improved.

The invention provides a multi-level alignment video text retrieval method and system based on a multi-modal large model. The method comprises the steps that S1, sparse sampling is carried out to obtain a video frame sequence needing to be used for retrieval; s2, extracting video feature representation and text feature representation; s3, obtaining a video-text global level similarity; s4, selecting a plurality of video-text expert multi-level alignment networks through a routing network; s5, the total loss is obtained through calculation, the model is adjusted and updated, and a target video text retrieval model is obtained; s6, according to the video text retrieval model obtained through training, similarity calculation is conducted on videos and texts used for retrieval and video text data in a database, and videos or texts which are related to the videos and texts and have the highest similarity are obtained; by applying the technical scheme, sparse and refined execution path selection of the retrieval task is realized, and the accuracy of video text retrieval is improved through multi-level semantic alignment.

A retrieval optimization method adaptive to multi-dimensional storage of power documents relates to the technical field of information retrieval, and comprises the following steps: firstly, preprocessing user query, identifying power business scenes and technical types of the user query, and extracting a query keyword set and a query vector; then, a three-level progressive retrieval strategy is adopted to retrieve in an electric power document library composed of a metadatabase and a vector data at the first level, candidate documents are screened based on keyword matching and business scenes; in the second stage, further filtering is carried out through similarity calculation of a query vector and a technical abstract vector; in the third stage, final accurate screening is completed in combination with full-text vector similarity and electric power professional rules; finally, information integration and structured output are conducted on the result, the problems that in traditional power document retrieval, the result is inaccurate, and efficiency is low are solved, and retrieval precision and response speed are remarkably improved.

The invention discloses an AI-based user experience data acquisition-analysis-evaluation integrated method, and relates to the field of intelligent analysis and evaluation, and the AI-based user experience data analysis-evaluation integrated method comprises the following steps: S1, collecting data, carrying out data preprocessing, and carrying out classification arrangement; s2, key tag parameters are extracted, a parameter rooting analysis structure is generated, and keyword tags are generated; s3, matching the historical experience script parameters with the keyword tags, and establishing a model; s4, training the model, and generating a personalized experience script; s5, scoring is carried out to generate a product experience scoring report, and verification optimization is carried out; and S6, outputting the personalized experience script and the keyword tag as an execution scheme. By introducing a natural language processing technology including semantic vectorization, vocabulary co-occurrence graph construction and semantic similarity calculation, the problems of user subjective data fragmentation, fuzzification, inconsistency and the like are solved.