182 results about "Precision marketing" patented technology

The embodiment of the invention provides a smart store data processing method and device based on multi-dimensional data analysis, which innovatively realizes alignment and mapping of spatial-temporal feature sequences by collecting sensor, intelligent terminal and social media data. And performing multi-dimensional data fusion based on the feature weight coefficient, and constructing a commodity recommendation model by using a convolutional neural network and a recurrent neural network. The system continuously optimizes the model performance through incremental learning, and realizes personalized commodity recommendation and store operation trend analysis. And meanwhile, a marketing decision optimization model is introduced, intelligent evaluation and dynamic optimization are performed on a marketing strategy, and the model is continuously updated through feedback data. According to the method, the limitation of traditional single data analysis is broken through, and a comprehensive data processing solution is provided for precision marketing of the smart store.

The invention relates to the technical field of social information, in particular to a social network influence prediction method and system. According to the method, brand-related users, content and interaction data are obtained from multiple platforms, a multi-dimensional influence scoring model is constructed, and initial influence scores of the users are calculated. And in combination with a social network cascade propagation theory, constructing a propagation probability calculation model, and predicting an information propagation range and user influence changes. And finally, generating an influence prediction report containing KOL ranking, hot topic analysis and optimal intervention opportunity suggestions. According to the method and system, through organic combination of a plurality of innovation points, the accuracy, comprehensiveness and practicability of prediction are remarkably improved, the existing technical problems are effectively solved, and a powerful support tool is provided for a brand to formulate a precision marketing strategy in a complex social media environment.

The invention relates to a precision marketing method and system based on real-time data flow. The method comprises the following steps: collecting customer behavior data in real time; preprocessing and dynamically marking the customer behavior data to generate preprocessed customer behavior data; performing real-time analysis on the preprocessed customer behavior data by using a deep learning algorithm, and generating a customer portrait in combination with historical behaviors, preference changes and situational features of the customer; according to the customer portrait and the customer real-time behavior, a marketing strategy is generated in real time through an intelligent decision routing technology, and recommendation content in the marketing strategy is dynamically adjusted based on the customer interaction behavior in the customer real-time behavior; according to the marketing strategy, the service recommendation corresponding to the marketing strategy is sent to the customer contact point, and through a real-time feedback mechanism, the marketing strategy is dynamically optimized according to the real-time response of the customer. The method has the effect of improving the marketing efficiency.

The invention relates to the technical field of e-commerce marketing, in particular to a product marketing service platform system and method, and the system comprises a user behavior tracking module, a consumption intention analysis module, a promotion time optimization module, an inventory monitoring and discount adjustment module, and a precision marketing push module. According to the method, the commodity access frequency and cross browsing data are extracted and analyzed in real time, the consumption trend of the user in a short period is rapidly identified, consumption tendency parameters are generated, potential purchase behaviors are accurately predicted by using browsing behaviors and an interaction mode, the recommendation accuracy is improved, the real-time inventory data are combined, a discount strategy and inventory management are dynamically adjusted, and the recommendation efficiency is improved. According to the method, the promotion activity is ensured to be closely matched with the inventory state, the resource utilization efficiency is improved, precise marketing pushing is performed according to the user equipment preference, the user interaction experience is enhanced, the sales growth is promoted, and the market response speed and the marketing effect are remarkably improved.

The invention relates to the field of knowledge graph construction, in particular to a customer archive knowledge graph construction method and system based on large model driving. The method comprises the following steps: encoding customer archives by using a large model technology, and fusing encoding results to generate entity twin vectors; constructing a knowledge graph by using structured data in the customer archive and the entity twin vector; the customer archive comprises an interaction log, and calculating an edge relation weight of the knowledge graph by using the interaction log; and adjusting the knowledge graph based on the edge relation weight. The method solves the problem that in the prior art, due to the fact that a traditional knowledge graph is difficult to accurately capture entity dynamic characteristics and implicit association, the constructed knowledge graph is difficult to meet the dynamic business requirements of real-time risk control, precision marketing and the like. And the interpretability and the reasoning ability of the knowledge graph are greatly improved.

The invention discloses an oral diagnosis and treatment patient service platform based on reinforcement learning, and relates to the technical field of diagnosis and treatment services, and the platform comprises a health scoring module which integrates multi-source data, generates personalized oral health scores through a dynamic weight distribution model, and displays high-risk items in combination with 3D visualization; the patient management and marketing module tracks patient behaviors, clusters and classifies the patient behaviors, generates a dynamic follow-up visit path by using multi-target reinforcement learning, and matches a precise marketing verbal skill through a knowledge graph; the in-diagnosis auxiliary module synchronizes health data of the patient to the doctor terminal in real time, detects prescription conflicts and performs early warning, and generates personalized communication verbal skills in combination with historical preferences of the patient and real-time emotion analysis; through multi-dimensional data integration and intelligent processing, the whole oral diagnosis and treatment process is optimized, the diagnosis and treatment efficiency and the patient satisfaction degree are improved, the operation cost is reduced, and remarkable innovativeness and practicability are achieved.

The invention discloses an artificial intelligence offline retail system and method, and relates to the technical field of artificial intelligence. According to the invention, through the integrated artificial intelligence offline retail system, the key technical problem of accurate recognition and tracking of customer behavior tracks by a computer vision technology in an entity retail scene is effectively solved; the customer behavior recognition and tracking capability in a complex shopping mall environment is improved, the system adapts to the diversity of customer behavior modes, and the system can obtain the position and moving track information of customers in real time and construct a preliminary portrait in combination with the appearance characteristics of the customers; in addition, the graph neural network and the knowledge graph technology are used for correlation analysis of multi-modal data, a comprehensive and accurate customer portrait is constructed, semantic user behavior understanding is realized, personalized service and accurate marketing capabilities are remarkably improved, and retailers can better understand and meet customer demands.

This invention relates to a system and method that enhances user interaction and marketing effectiveness through advanced data integration and personalized algorithms. The system and method collects detailed user data through body scanning and questionnaires, creating comprehensive profiles using machine learning. These profiles facilitate precise matching of influencers with businesses based on physical attributes, audience demographics, and content preferences. The system generates AI-driven marketing personas tailored to specific demographics and enables live shopping integration with social platforms. A novel double-blind review mechanism ensures transparent feedback between businesses and influencers. The platform supports group profiles that aggregate multiple users' data, enabling family-based shopping with privacy controls and preference weighting. The system continuously refines its algorithms using campaign performance data and engagement metrics, optimizing future matches and marketing strategies. This innovative approach significantly improves marketing ROI, user engagement, and personalization for digital social interaction and targeted marketing.

The invention belongs to the technical field of precision marketing and intelligent recommendation, and discloses a B2B customer deep insight and precision reaching method based on a multi-modal large model, and the method comprises the specific steps: S1, carrying out the fusion and deep analysis of multi-modal data; s2, performing customer multi-dimensional preference modeling; s3, an intention and emotion analysis engine; s4, generating a personalized marketing strategy; s5, recommendation execution and verbal skill optimization; s6, feedback collection and reward function drive optimization; and S7, applying a cold start solution and transfer learning. According to the invention, through integration of multi-mode data of client official websites, news information, social media dynamics, mail communication, product images and conference recording, panoramic insight of clients from a business level to behavior details is realized; in combination with deep analysis of a pre-trained large model on texts, images and audios, explicit demands can be obtained, and industry features, cultural characteristics and potential concerns of customers can be understood.

The invention discloses an electronic commerce precision marketing intelligent system and method fusing AI personalized recommendation. The system comprises a multi-source data acquisition module, a data preprocessing module, a user portrait construction module, a real-time behavior monitoring module, a recommendation algorithm module, a marketing scheme generation module, a marketing effect evaluation module and a feedback optimization module. According to the method, the comprehensive user portrait is constructed by integrating the multi-dimensional user data, and the mixed recommendation algorithm is applied, so that the user requirements can be deeply mined, accurate personalized recommendation is realized, and the matching degree between the recommendation result and the actual requirements of the user is improved; by adopting the real-time behavior monitoring module and the recommendation algorithm module, the recommendation strategy can be updated in time according to the real-time behavior of the user, so that the recommendation better fits the current shopping intention of the user; by adopting the marketing scheme generation module, diversified marketing schemes can be generated, an effective marketing effect evaluation and feedback optimization mechanism is combined, marketing activities can be continuously optimized, and the marketing efficiency and income of merchants are improved.

The invention discloses an intelligent recommendation precision marketing method and system based on a large model, and belongs to the technical field of artificial intelligence and retail, and the method comprises a data perception layer which is used for collecting user related information and comprises a face recognition terminal, an Internet of Things sensor, a CRM system and transaction POS data; the intelligent decision-making layer is used for performing fusion modeling and recommendation strategy generation on the sensing data and comprises a dynamic image engine, a demand prediction large model and a recommendation strategy generator; and the execution feedback layer is used for sending the recommendation result to the user in a multi-channel manner and recycling feedback data, and comprises a shop assistant PAD push module, an electronic price tag updating module and a self-service terminal guide module. The repurchase rate and customer unit price of a retail scene can be remarkably improved, and the technical problem that a traditional retail robot cannot adapt to complex requirements of customers is solved.

The invention discloses an AI Agent marketing system and method based on a large model, and the method comprises the steps: carrying out the automatic call-out and conversation through a call-out robot according to an intention customer list, and obtaining the contact information of a customer according to the conversation content; starting a chat robot according to the customer contact information, and guiding the customer to activate a voice service by using the chat robot; constructing a customer portrait according to the historical dialogue content, analyzing a customer intention level, matching a recommendation scheme from a marketing scheme database according to the customer portrait and the customer intention level, and obtaining a recommendation verbal skill by using a chat robot; and pushing the recommended verbal skill and the recommended scheme to a worker for manual follow-up. The invention relates to the technical field of intelligent marketing, and solves the problems of inaccurate customer intention recognition, unreasonable marketing resource allocation, poor customer experience and the like in an existing marketing method.

The invention discloses a precision marketing recommendation system based on an AI large model, relates to the technical field of information recommendation, is used for improving the problem of product information recommendation efficiency reduction caused by tag recommendation delay, and comprises the steps of collecting user data, analyzing user information frequency modulation potentials by using a K nearest neighbor algorithm, performing classification, screening out users with high frequency modulation potentials, and performing recommendation on the users with high frequency modulation potentials. The method comprises the following steps: setting detection time to collect recommendation information data of a user, screening key tags from the recommendation information data, calculating a tag demand coefficient, detecting a user emptying behavior, carrying out secondary screening on the key tags to obtain evaluation tags, and judging whether demand down-regulation processing is carried out on the evaluation tags by utilizing a corrected geometric average method. And marking the evaluation labels requiring down-regulation processing, obtaining label attributes of the marked labels, calculating down-regulation coefficients of different marked labels according to the label attributes of the marked labels and the label demand coefficients, and setting skipping time for the marked labels according to the down-regulation coefficients.

The invention relates to the technical field of artificial intelligence, can be applied to business scenes of financial science and technology, medical health and the like, and discloses an object portrait label set generation method, device and equipment and a medium, and the method comprises the steps: collecting and integrating multi-source data, generating a multi-modal data set, and cleaning and enhancing the data set; extracting a portrait label set based on the historical structured information of the object, obtaining a multi-modal cue word set, and constructing a unified representation sequence; inputting the unified representation sequence into a multi-modal model, obtaining fusion representation and extracting a derivative factor candidate set; and performing consistency verification on the cross-modal data to obtain a consistency factor set, and finally fusing the consistency factor set with the structured portrait label set to generate a unified portrait system and output an object portrait label set. According to the method, the accurate and comprehensive customer portrait is generated by combining multi-modal data and cross-modal consistency verification, the portrait accuracy is improved, and support is provided for accurate marketing and personalized service.

The invention relates to the technical field of advertisement putting, in particular to an AI-based accurate marketing advertisement putting method, which comprises the following steps: collecting multi-modal sensing data of a user terminal, extracting behavior information such as facial micro-expression, touch screen pressure and equipment rotation angular velocity, and generating an emotion feature vector through a pre-trained emotion classifier; matching the emotional characteristics with an emotional labeling matrix of the advertisement materials, and selecting the advertisement materials of which the emotional adaptation degree is higher than a threshold value to construct a candidate advertisement set; duration matching is carried out in combination with the user attention attenuation curve and the advertisement material display duration, and it is ensured that putting is completed before the attention critical point; when the user skips the advertisement, pupil focusing positions, finger tracks and acceleration information are extracted to generate a negative feedback feature matrix, the weight of the emotion classifier is dynamically updated, and a matching threshold value is optimized. According to the invention, an accurate delivery mechanism of emotion driving, attention perception and negative feedback self-learning is realized, and the personalized matching degree and delivery effect of advertisements are significantly improved.

The invention discloses a knowledge graph construction and application method based on the insurance industry, and belongs to the technical field of industry knowledge graphs. Comprising the following steps: S1, data collection and arrangement; collecting multi-source data of the insurance industry, and sorting the data; s2, knowledge modeling: defining an entity, determining a relationship, and performing attribute setting; s3, constructing a map: performing entity recognition and disambiguation, performing relation extraction, and storing a map database; s4, map optimization: adding semantic information, optimizing a relationship and creating an index; and S5, applying the atlas. According to the invention, multi-source data of the insurance industry is collected, and more comprehensive and accurate information support is provided for insurance business; according to the technical scheme, multiple application methods of the knowledge graph are provided, insurance business links such as risk assessment and management, customer portrait and precision marketing, intelligent customer service and knowledge questions and answers, product design and optimization, claim settlement management and the like are covered, the practical value of the knowledge graph is realized, and the efficiency and effect of each business link can be effectively improved.

The invention provides an ai large model driving-based intelligent marketing method and system. The method comprises the following steps of: extracting a behavior feature vector by adopting a multi-scale behavior mode coding network based on a micro interaction behavior of a user; core marketing interests of the users are extracted by adopting self-adaptive multi-layer interest clustering, and behavior tags are generated; analyzing the real influence of different marketing strategies on the purchase decision of the user by using double-layer causal reasoning, and outputting a personalized marketing effect label; the applicability of the marketing strategy is scored based on the personalized marketing effect label, and dynamic recommendation strategy optimization is carried out by adopting a reinforcement learning framework; and pushing personalized marketing content to the user according to a personalized marketing strategy distribution result, and optimizing a recommendation strategy by adopting a dynamic closed-loop feedback optimization model based on real-time feedback of the user. According to the method, innovative technologies such as fine-grained behavior analysis, causal reasoning optimization and dynamic marketing strategy adjustment are introduced, so that the limitation of an existing precision marketing technology is overcome, and the recommendation precision and the marketing ROI are improved.

The invention relates to a financial customer precision marketing system and method based on big data and artificial intelligence in the field of financial science and technology and intelligent marketing cross technology, and provides a financial customer precision marketing system and method based on big data and artificial intelligence by integrating multi-dimensional customer data, constructing a dynamic customer portrait, predicting customer demands by using a deep learning model and combining the real-time reaching capability of enterprise WeChat ecology. According to the method, automatic execution and closed-loop optimization of a personalized marketing strategy are realized, and the customer conversion rate and loyalty of a financial institution are improved; the method is innovatively embodied in deep combination of an emotion recognition technology and a financial scene, reinforcement learning optimization of a dynamic rule engine and a privacy compliance data desensitization mechanism, and solves the problems of lagging customer demand recognition and low strategy matching efficiency in traditional marketing.

The invention belongs to the technical field of electronic commerce, and particularly relates to a long-tail commodity recommendation method based on precise symmetric positive definite manifold learning. Comprising the steps that user behavior data and commodity feature data of an e-commerce platform are acquired and preprocessed, and a commodity feature matrix and a user commodity interaction matrix are obtained; optimizing the user commodity interaction matrix by adopting a low-rank approximation technology, and updating the user commodity interaction matrix; mapping the commodity feature matrix into a symmetric positive definite matrix space, and performing manifold embedding processing to obtain an SPD feature vector of the commodity; carrying out aggregation processing on the SPD feature vectors of the commodities by adopting a popular full-connection layer to obtain aggregation features; mapping the aggregated features to Euclidean space by using a geometric preserving mapping function to obtain mapping features; processing the mapping features to obtain a long-tail commodity recommendation result; according to the method, the advertisement click rate of the e-commerce platform is improved, and the precision marketing and recommendation effects are remarkably enhanced.

The invention provides a marketing system based on multi-modal literature data intelligent analysis, and the system comprises the steps: automatically recognizing and analyzing various formats of scientific research literatures through a literature analysis and preprocessing module, and extracting structured information; the AI enhancement analysis module deeply mines literature semantics by using a large model; the data processing and integration module cleans and integrates the information, constructs a researcher portrait including five dimensions of identity, interest, technology, equipment and demand prediction, and further generates a personalized marketing strategy; the cache management module optimizes result storage and retrieval efficiency, and the batch processing module coordinates a multi-file parallel processing flow; according to the system, the automatic conversion from the original literature to the precision marketing strategy is realized. According to the method, deep mining and precise marketing of the multi-source scientific research literature are realized, and the information extraction accuracy and the marketing conversion efficiency are remarkably improved.

The invention relates to the technical field of advertisement marketing, in particular to an advertisement marketing system for after-sales data statistics. The system comprises a data acquisition module, an intelligent analysis module, a precision marketing module and an evaluation feedback module. The customer basic information, purchase information, after-sales behavior data and customer feedback data are acquired through the data acquisition module, the intelligent analysis module establishes a recurrent neural network model for predicting the customer loss probability, and the precision marketing module performs precision marketing by taking the customer loss probability predicted by the intelligent analysis module as the horizontal axis and the customer value as the longitudinal axis. The method comprises the following steps: establishing a probability-divided two-dimensional matrix, formulating a corresponding advertisement marketing strategy, adopting different advertisement marketing strategies for different types of customers, improving pertinence, redeeming the customers to the greatest extent, reducing enterprise loss, establishing an evaluation system by an evaluation feedback module, evaluating the effect of advertisement marketing activities at regular intervals, and improving the efficiency of advertisement marketing. And the advertisement marketing strategy is continuously optimized.

The invention relates to the technical field of big data and retail management, in particular to a retail commodity precision marketing strategy making system which comprises a data recording module, a commodity life cycle intelligent management module, a preferential binding planning module and a strategy feedback adjustment module. The data recording module is used for recording the purchase time, the purchase quantity, the real-time stock quantity, the off-shelf duration, the sold-out time, the daily sale quantity and the total sales volume of the commodities by the user; the commodity life cycle intelligent management module is used for displaying strategy information of hot commodities and unsalable commodities; the preferential binding planning module is used for displaying binding strategy information of corresponding bound sales commodities; and the strategy feedback adjustment module is used for displaying adjustment strategy information of the corresponding bundled sales commodities, so that the risks of stock overstock and stock shortage are reduced, the stock turnover efficiency is improved, the sales opportunities of unsalable commodities are increased, and it is ensured that the bundling strategy can flexibly cope with market demand changes.

The invention discloses a smart marketing system and device and a medium, and belongs to the field of big data analysis. The system comprises a data support layer used for collecting multi-source heterogeneous data including equipment data, user behavior data and third-party data, and forming data assets by preprocessing the multi-source heterogeneous data; the intelligent decision-making layer is used for identifying the life cycle stage of the current user based on an intelligent model matrix and the data assets, predicting the transition stage of the life cycle of the user and generating a precision marketing strategy and a channel selection scheme of each stage; and the business application layer is used for converting the precision marketing strategy and the channel selection scheme into a specific marketing task, monitoring marketing effect data and feeding back the marketing effect data to the data support layer and the intelligent decision-making layer. According to the system, the intelligent marketing process of full-link data fusion, dynamic life cycle management and automatic marketing closed loop can be realized.

The invention discloses an intelligent delivery targeted penetration decision-making method for precision marketing, particularly relates to the technical field of intelligent delivery targeted penetration decision-making, and comprehensively identifies potential anomalies through unified processing of a standardized index set and dynamic association construction of multi-level thresholds; by means of the constraint of a delivery strategy oscillation index on a delivery behavior, dynamic bidding, material optimization and flow distribution can be smoothly adjusted in a fluctuating environment, the controllability of budget consumption and the continuity of a delivery effect are ensured, and a multi-dimensional penetration strategy of competition, scenes, crowds and materials is combined, so that the delivery efficiency is improved. According to the method, high-value user groups are more accurately reached, the material matching degree is improved, the return on investment and the conversion rate are stably kept increased, oscillation and resource waste in the putting process are reduced, and the cross-platform collaborative optimization capability and the risk prevention and control level are improved.

The invention discloses a marketing management method and system based on big data analysis, and relates to the technical field of marketing management, and the method comprises the steps: analyzing the behavior data of a marketing target user, and constructing a marketing target user portrait; outputting a marketing strategy of the marketing target user and the associated user through the behavior record of the associated user in combination with the portrayal analysis of the marketing target user; adjusting marketing strategies of the marketing target user and the associated user based on the market data; the adjusted marketing strategy is applied to market activities, and the marketing process and the market risk condition are monitored in real time; marketing achievements of all stages are obtained based on real-time monitoring, the marketing achievements are analyzed, a marketing change trend is recognized, a marketing strategy optimization mechanism is triggered based on the marketing change trend, and a marketing strategy is adjusted and optimized. Accurate marketing, strategy dynamic adjustment and resource optimal configuration are realized, and the marketing efficiency and effect are remarkably improved.

The invention relates to the technical field of rural e-commerce marketing, and discloses a rural e-commerce precision marketing method based on big data, and the method comprises the steps: dividing user data sets of all regions based on the geographic positions and consumption records of users, including purchase and browsing behavior data; determining a user type according to the purchase frequency and the commodity preference; generating a marketing strategy set and determining an execution sequence; generating recommended commodities and a promotion activity list in combination with user types, stock and the like; and associating the data to generate a precision marketing scheme. The method further relates to user data boundary determination, user type subdivision, marketing strategy dynamic adjustment, consumption potential grade division, personalized recommendation strategy generation, marketing scheme verification and correction and the like. According to the method, accurate classification and regional differentiation marketing of users are realized through big data mining, the strategy is dynamically optimized, the marketing effect and the resource utilization rate are improved, and the method is suitable for accurate operation of a rural e-commerce platform.

The invention discloses a tourist behavior multi-modal data fusion and precision marketing method based on AI and a block chain, and the method comprises the following steps: firstly, fusing multi-source heterogeneous data through a federated learning technology, constructing a tourist multi-modal feature vector on the premise of protecting data privacy, and solving a cross-platform data island problem; secondly, designing a hierarchical authority control mechanism based on an alliance chain technology, and realizing safe sharing and compliance use of sensitive data by adopting homomorphic encryption and zero-knowledge proof; and finally, combining a reinforcement learning model and a multi-objective optimization algorithm to dynamically generate a personalized recommendation strategy, and realizing second-level response and strategy iteration through an online learning mechanism. The method provides safe, accurate and adaptive marketing decision support for the smart scenic spot.

The invention belongs to the technical field of causal inference, and discloses a causal inference-based content preference crowd selection method, which comprises the following steps of: A1, selecting a user group; a2, dividing user groups; a3, a Persuadable crowd is located, and the Persuadable crowd is located; and A4, obtaining a UPLift value. The invention provides an innovative crowd selection scheme which is specially designed for coping with a complex and changeable new scene, the scheme can reasonably optimize resources from the global perspective, and meanwhile, the scheme also has certain self-optimization capability, continuously improves the selection effect, and is suitable for popularization and application. The scheme also plays an important role in making strategies such as content recommendation, search and push in a support platform, the platform can more effectively push contents interested by users and improve user experience and satisfaction through accurately delineating user groups, in addition, the scheme also provides powerful support for accurate marketing and refined operation, and the user experience and satisfaction are improved. And the platform is helped to realize more efficient user conversion and income increase.

The invention discloses an agricultural product precision marketing system based on big data, and relates to the technical field of agricultural informatization. Comprising a vector construction module which is used for constructing environment kinetic energy characteristics according to acquired original data and constructing a multi-dimensional agricultural effect vector according to the environment kinetic energy characteristics; the environmental effect field generation module is used for inputting the input data into a preset required fluctuation energy calculation model, outputting required fluctuation energy and synthesizing an environmental effect field based on the required fluctuation energy; the dynamic regulation and control module is used for constructing a resource allocation function based on the normalized environmental effect field and the inventory rate to carry out dynamic resource allocation optimization, and calculating to obtain an optimal resource allocation value; and the strategy execution module is used for optimizing the resource allocation strategy and the agricultural product price according to the optimal resource allocation value and executing regulation and control. Through more accurate demand prediction, dynamic resource allocation and real-time feedback learning, the resource utilization efficiency is optimized, and the overall efficiency of agricultural product production, inventory management and marketing is improved.

The invention relates to the technical field of data analysis, in particular to a bank merchant value evaluation method and device based on big data, a medium and equipment, and the method comprises the steps: collecting bank customer original data based on the big data; missing values are filled through preprocessing, repeated data are removed, and classification variables are numeralized: binary variables are kept in an original state, multi-valued class variables are subjected to one-hot coding, and ordered class variables are subjected to label coding; performing Z-score standardization on all numerical value characteristics; the dominant and implicit indexes are fused to construct a feature vector, the implicit indexes are obtained through customer behavior data quantification, and differences between samples are measured through a weighted Euclidean distance; initial clustering is carried out on the features, samples are classified into the nearest center point cluster, and iteration updating is carried out until convergence; and performing density clustering in each initial cluster, determining a core point, a boundary point and a noise point by calculating an average distance and a nearest neighbor distance in the cluster, and performing repeated expansion to form a stable sub-cluster. And finally, combining the initial cluster with a density division result, if the initial cluster is not subdivided, reserving the original cluster, and if the initial cluster is subdivided, removing noise points by taking a sub-cluster as a criterion, thereby obtaining a final value label of the customer. According to the method, the interpretability and the calculation efficiency are considered while the model precision is ensured, and the technical capabilities of banks in the aspects of precision marketing, risk control management and merchant asset value mining are improved.