147 results about "Precision marketing" patented technology

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.

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 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 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 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 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 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 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.

The invention discloses an intelligent video editing optimization system and method based on target audience prediction, and belongs to the technical field of artificial intelligence and digital marketing. According to the method, dynamic analysis of target audience features and real-time adaptation of a video editing strategy are realized through a programmed automatic process, and the problems of high manual dependency, poor cross-culture adaptability, strategy updating lagging and the like in the traditional technology are solved. The method specifically comprises the following steps: calling a third-party platform application program interface to obtain user behavior data, and generating a dynamic audience portrait by using an artificial intelligence large model; shot selection, duration distribution and culture adaptation of video content are optimized through a reinforcement learning algorithm on the basis of portraits, a shot selection module dynamically distributes technical parameter close-up and cost comparison content according to user role types, and a culture adaptation module calls a taboo library to automatically replace sensitive elements; a synchronous test is carried out by generating multiple versions of videos, closed-loop feedback iteration is realized based on a user click rate and a conversion rate index, and an editing strategy is continuously optimized. Compared with a traditional method, the system has the advantages that the cross-culture adaptation precision, the strategy response speed and the user conversion effect of the video content are remarkably improved through cooperation of the artificial intelligence model and the programmed decision engine, and the system is suitable for precision marketing requirements of multiple scenes such as cross-border e-commerce and industrial equipment.

The invention discloses a user behavior analysis and precision marketing system based on deep learning, and relates to the technical field of computers, and the system comprises the steps: S1, carrying out data access and management, collecting a user behavior log, a session sequence, position information, an end-side event and cross-domain Internet of Things data, combining a commodity portrait, a commercial tenant portrait and multi-modal content, and carrying out the recognition of the user behavior log, the session sequence, the position information, the end-side event and the cross-domain Internet of Things data; time alignment, anomaly repair, label completion and privacy desensitization are completed, and a unified data basis is formed. According to the method, seamless integration and high-quality processing of multi-source heterogeneous data are realized through unified data access and treatment capability, a solid data foundation is laid for subsequent deep learning and precision marketing, the problems of data dispersion and poor quality of an existing system are effectively solved, and the method has a good application prospect through multi-modal representation learning and feature construction. Text, image and video features are extracted by using a depth encoder, and cross-modal feature alignment and complex relation modeling are realized in combination with sequence modeling and a graph neural network.

The invention belongs to the technical field of user portrait analysis, and mainly relates to a cross-border e-commerce user portrait analysis method based on big data, and the method comprises the steps: extracting a user demand tag through multi-source data collection and standardization processing in combination with a semantic understanding model, and constructing a compliance knowledge graph of a target market; in the construction of user portraits, a framework based on cultural cognition is adopted, and preferences of users on types and components of health care products under different cultural backgrounds are deeply mined; a semantic network between a culture background and health care product attributes is constructed through a knowledge graph technology, and the recognition ability of the model to culture sensitive features is further improved; by dynamically updating the user portraits and combining marketing feedback data, commodity recommendation and marketing strategies are continuously optimized, so that the accurate marketing effect of cross-border e-commerce is improved on the premise of guaranteeing compliance; the user experience and the conversion rate of the cross-border e-commerce platform can be effectively improved, and the risk of violation is reduced.

The invention belongs to the field of information systems, and particularly relates to a market promotion community accurate operation system based on social network behavior analysis. The system comprises a data acquisition module, a preprocessing module, a user behavior analysis module, a community member classification module, a precision marketing recommendation module, an information spreading optimization module, a community activeness evaluation module, a feedback optimization module and the like. Through two innovative algorithm formulas of a user interest modeling algorithm based on a topic model and a user influence evaluation algorithm based on a graph neural network, deep analysis and accurate classification of user behaviors are realized. Personalized recommendation can be provided for the user, an information dissemination strategy is optimized, the community activeness is evaluated in real time, the system is optimized according to feedback, and the market promotion effect and the community operation efficiency are effectively improved.

The invention relates to the technical field of precision marketing analysis based on big data, and discloses a precision marketing analysis method and system based on big data. Sequentially executing transaction log preprocessing, extracting a customer transaction sequence and constructing a commodity transfer frequency matrix; constructing a commodity transition probability matrix based on the matrix, and calculating a diffusion retention coefficient; the commodity popularity is initialized in combination with a time attenuation mechanism; constructing a directed purchase network and calculating the diameter of the directed purchase network as an upper limit of diffusion round times; executing heat diffusion iteration according to the transition probability and the retention coefficient, and judging whether convergence occurs or not; and finally normalizing the popularity vector and sorting, and outputting a precise putting target. According to the method, modeling behaviors are extracted from original data, a propagation network is constructed, and indirect propagation of user interests is simulated in combination with time sequence popularity, so that automatic modeling from behavior data to a delivery target is realized. According to the scheme, the accuracy and the service decision-making efficiency are improved.

The invention discloses a precision marketing label screening method and system based on high-dimensional interactive screening. The method comprises the following steps: S1, a user determines a core index variable y for measuring a service effect; s2, a user determines a to-be-screened label range and preliminarily selects a proper customer group to obtain a sample set x; s3, constructing a hierarchical screening model; and S4, the user selects a screening hierarchical mode: weak hierarchical structure screening and strong hierarchical structure screening. The system comprises an interactive display module, a data storage module, a screening analysis module and a feedback optimization module. According to the method, reference can be provided for bank marketing personnel when the marketing objects are determined, the situation that labels with information values are omitted when the labels are selected from numerous labels is avoided, and hidden interaction information among the labels can be explored.

The application discloses a cost-optimized out-of-season clothing renovation design method and an intelligent processing system, relates to the field of clothing intelligent design systems, and has the technical scheme as follows: a data acquisition module, which collects original data of out-of-season clothing and popular trend matching accurate marketing landing; a data analysis and prediction module, which evaluates potential risks and feasibility in the renovation process based on the collected data; a design scheme generation module, which automatically generates multiple feasible renovation design schemes according to the data analysis results and personalized requirements input by a user, and determines the best design scheme; and the application integrates a complete closed-loop process from data acquisition to evaluation, combines an artificial intelligence deep learning algorithm to complete demand prediction, design scheme generation and optimization, and significantly improves production efficiency and reduces artificial dependence.

The invention discloses an AI-based tobacco consumer behavior analysis and personalized marketing method and system, and relates to the technical field of data processing, and the method comprises the steps: collecting a multi-modal real-time behavior data stream; performing dynamic noise filtering on the multi-modal real-time behavior data stream to obtain an effective behavior sequence; extracting a plurality of spatio-temporal feature vectors of the effective behavior sequence, fusing the spatio-temporal feature vectors and compressing dimensions to obtain a target feature tensor; inputting the target feature tensor into an incremental learning engine for incremental learning; according to the public opinion event, performing matching in a preset marketing strategy library to obtain alternative actions, and then pushing differentiated marketing strategies; feedback behavior data is collected, and then a strategy utility index is calculated according to the feedback behavior data, and the strategy utility index is transmitted back to the incremental learning engine to serve as a regularization constraint term of next round of updating. According to the method, the incremental learning engine, the public opinion event and the marketing strategy mapping mechanism are combined, and dynamic optimization and precise marketing of the incremental learning engine are achieved.

The invention discloses a consumption loan marketing method, apparatus and device, and a computer program product. The method comprises the steps of obtaining multi-dimensional basic information and consumption loan behavior data of a customer; determining a customer group type to which the customer belongs according to the multi-dimensional basic information; according to the consumption loan behavior data of the customer, determining a life cycle stage and a limit use condition of the customer; determining a target consumption loan preferential strategy corresponding to the customer by using a dynamic consumption loan preferential strategy according to the customer group type of the customer, the life cycle stage of the customer and the limit use condition; and according to the target consumption loan preferential strategy, contacting the customer by using the preferential contacting channel of the customer. According to the method and the system, the customers are subjected to refined grouping and life cycle stage division, and a dynamic preferential strategy and an intelligent contact channel decision are combined, so that personalized preferential strategies can be provided for different customer groups and stages, accurate marketing of coupons in bank consumption loan business is realized, and the conversion rate of consumption loan customers is improved.

The invention discloses a machine learning recommendation method for precision marketing and potential customer mining. The method comprises the following steps: S1, data acquisition: establishing a user tag table based on historical data and acquiring user behavior characteristics; s2, data preprocessing: preprocessing the data, including removing abnormal values, filling missing values, and converting category features into numeric features; s3, automatic box separation: performing variable analysis and automatic box separation on the preprocessed data; s4, feature processing: converting a data table obtained by binning into a numerical value form; s5, dividing a data set: dividing the processed data set into a training set and a test set; s6, stepwise regression is carried out to select features: stepwise logistic regression is used to select model key variables; s7, manual binning and modeling data set preparation are carried out; the method has the characteristics of high automation level and high decision-making efficiency.

A training method of a marketing prediction model for eliminating time deviation and a precision marketing method relate to the technical field of data processing, samples are grouped by setting time stamps and marketing scheme stamps, experimental samples and contrast samples in the same time group are ensured to be comparable in average value, and the accuracy of marketing prediction is improved. Therefore, the time deviation problem when historical samples are adopted is eliminated, meanwhile, purchase rate target pre-training is carried out on the model to solve the problem that high-latitude features cannot be effectively utilized due to the fact that the unbiased sample size is small, and finally the overall loss is calculated in a multi-task training mode, so that the accuracy of the model is improved. The marketing prediction model can achieve better prediction accuracy under the condition that biased samples and a small number of unbiased samples are adopted, so that the sampling cost caused by only using the unbiased samples is greatly reduced, and both the cost and the effect are achieved. By using the marketing prediction model, accurate putting of marketing schemes can be further realized, and the marketing prediction model has a good practical value.

The invention relates to the technical field of insurance information integration, and discloses an insurance information integration method. The method comprises the following steps: reading annual insurance premium and total claim settlement amount of a customer, calculating and normalizing an extreme value, and eliminating dimensional difference; constructing a customer-insurance policy-risk three-layer edge set, generating a risk label set, calculating a similarity matrix according to an intersection-to-union ratio, and adaptively setting a community merging threshold value by using a global average value; clients are initialized into communities, high-similarity communities are iteratively merged, and the number of groups does not need to be preset; aggregating the average insurance premium and claim settlement of each community, and calculating a risk coverage ratio and homogeneity; and counting cross-community over-threshold similar customer pairs, comparing the cross-community over-threshold similar customer pairs with a mean value to identify potential association, and marking high-premium and high-claim communities. The process unifies and disperses data, eliminates dimensional differences, self-adapts threshold values and groups, generates community portraits, and supports precision marketing and joint risk control.

The invention discloses a bank financial cloud customer potential tapping method based on machine learning. The mining method comprises the steps of S1, defining sample data; s2, performing customer portraying according to the sample data; s3, carrying out data processing on the customer portrait; s4, carrying out model construction and verification; s5, the model is applied; and step S6, evaluating the model. Through a LightGBM ensemble learning algorithm, a complex relationship between customer characteristics and purchase tendencies can be automatically learned, a response probability of a customer is output, and accurate marketing of the customer is realized. And customer omission caused by potential customer mining through artificial experience is avoided.