An AIGC technology-based full-channel AI automatic customer acquisition system for the finance and tax industry

Abstract

The application discloses a kind of full-channel AI automatic customer acquisition systems of finance and tax industry based on AIGC technology, belong to data processing and business method field, it includes constructing initial image feature library, and is updated using the customer behavior data collected, generates target image feature library, extracts content preference features, generates real-time hot feature and content creation guide parameter, inputs AIGC content generation model, outputs preliminary marketing content and carries out analysis, generates compliance marketing content entity, analysis is carried out in combination with target image feature library, generates channel adaptation degree matrix, distributes and cross-channel automatic placement are carried out for compliance marketing content entity, obtain customer interaction data, feedback data is extracted and input into target image feature library.The application adopts the technical path that dynamic image construction, content intelligent generation, channel accurate adaptation and feedback closed loop update are combined, realizes the automation and intelligentization of finance and tax industry customer acquisition whole process, and continuously optimizes the accuracy of marketing strategy.

Description

Technical Field

[0001] This invention relates to the field of data processing and business methods, and in particular to an AI-automated customer acquisition system for the finance and tax industry based on AIGC technology across all channels. Background Technology

[0002] Driven by the digital wave, customer acquisition has become a core element of market competition in the financial and tax services industry. Digital marketing, as a crucial bridge connecting service providers and potential customers, directly impacts business growth through its efficiency and accuracy. With the rapid development of AI-generated content (AIGC) technology, its potential in automated content creation is being applied to marketing activities across various industries, providing new technological possibilities for improving customer acquisition efficiency in the financial and tax sector.

[0003] Currently, financial and tax service companies typically employ a multi-platform strategy when acquiring online customers. The marketing team uses data from the customer relationship management system to initially segment customers, and then, based on team members' experience and market assessment, manually or semi-automatically publishes marketing content across multiple channels such as social media and industry websites. This content is often a fine-tuning of a generic template, aiming to reach potential customers broadly, and relies on analytics tools provided by third-party platforms to evaluate the effectiveness of individual campaigns.

[0004] Existing technologies have several shortcomings in practical applications. First, the mechanisms for building and updating customer profiles are relatively outdated, relying heavily on historical transaction data and static information. This makes it impossible to capture real-time changes in customer interests displayed on public channels, leading to a disconnect between marketing content and actual customer needs. Second, content production efficiency is low and homogenization is severe; manual creation struggles to meet the demands of multi-channel, high-frequency, and personalized content. Third, the selection of distribution channels largely depends on subjective experience, lacking a quantitative evaluation system for the matching degree between content, channels, and audiences, making it difficult to optimize the allocation of marketing resources. Finally, there is a disconnect between the evaluation of marketing campaign effectiveness and strategy adjustments, failing to generate effective feedback to guide the automatic adjustment and optimization of subsequent strategies. Summary of the Invention

[0005] To address the aforementioned issues, this invention provides an AI-automated customer acquisition system for the finance and tax industry across all channels, based on AIGC technology. It employs a combined technical approach of dynamic profile building, intelligent content generation, precise channel adaptation, and feedback loop updates. This system automates and automates the entire customer acquisition process for the finance and tax industry, and continuously optimizes the accuracy of marketing strategies.

[0006] The above objectives can be achieved through the following approach: A fully automated AI-powered customer acquisition system for the finance and tax industry based on AIGC technology includes: a profile building module, used to acquire publicly available business data from the finance and tax industry and a preset customer tag system to build an initial profile feature library; a customer behavior data collection module, used to dynamically update the initial profile feature library and generate a target profile feature library; a preference hotspot module, used to extract content preference features of the target customer group based on the target profile feature library, and analyze interactive hotspots in the customer behavior data in real time to generate real-time hotspot features; and a content generation module, used to input the content creation guidance parameters into a preset AIGC content generation model to drive the AI... The GC content generation model outputs preliminary marketing content and performs standardization verification and professional terminology enhancement on the preliminary marketing content to generate compliant marketing content entities. The channel adaptation module analyzes the dissemination characteristics and audience matching degree of the compliant marketing content entities on different online channels based on the target profile feature library, generating a channel adaptation matrix. The intelligent delivery module allocates delivery channel sequences to the compliant marketing content entities according to the channel adaptation matrix and executes cross-channel automated delivery according to the delivery channel sequences to obtain customer interaction data. The feedback update module extracts conversion behavior features and negative feedback features from the customer interaction data and inputs them as feedback data into the target profile feature library to initiate an update of the target profile feature library.

[0007] Optionally, the profile building module includes: a data acquisition unit, used to periodically crawl public posts, comments, and articles containing financial and tax keywords from social media platforms and industry information websites to obtain raw text data; a behavior analysis unit, used to perform entity recognition and sentiment analysis on the raw text data to generate customer behavior data; an association matching unit, used to associate and match the customer behavior data with customer tags in the initial profile feature library and calculate the matching degree weight; and a profile updating unit, used to update the initial profile feature library with new topics of interest and sentiment tendencies in the customer behavior data as new feature dimensions based on the matching degree weight, forming a target profile feature library.

[0008] Optionally, the preference hotspot module includes: a template generation unit, used to parse structured elements of historical content that meets a preset interaction rate from the content preference features, and generate a content template skeleton; a hotspot extraction unit, used to extract fiscal and tax policy keywords and frequently asked phrases that appear more frequently than a preset frequency within the current time period from the real-time hotspot features, and generate a set of hotspot keywords; and a parameter encapsulation unit, used to combine the content template skeleton and the set of hotspot keywords, and add the content tone and style requirements obtained from the target profile feature library, and encapsulate them into content creation guidance parameters.

[0009] Optionally, the content generation module includes: a layered generation unit, used to decompose the content creation guidance parameters into structural constraints, keyword constraints, and tone constraints, and input them into different generation layers of the AIGC content generation model to generate preliminary marketing content layer by layer; a compliance enhancement unit, used to perform financial and tax terminology consistency verification on the preliminary marketing content and bind policy-related expressions to corresponding legal sources to generate enhanced preliminary marketing content; and a filtering and splitting unit, used to split the enhanced preliminary marketing content into content fragments and perform compliance screening and negative word filtering to generate compliant marketing content entities.

[0010] Optionally, the channel adaptation module includes: a channel profiling unit, used to extract the profile features of the historical audience of each online channel from the target profile feature library, and generate a channel audience feature vector; a content feature unit, used to extract features from the compliant marketing content entity, and generate a content feature vector; and a scoring adaptation unit, used to calculate the cosine similarity between the content feature vector and each channel audience feature vector, and combine it with the historical content dissemination attenuation factor of each channel to obtain the matching score of each channel, thus forming a channel adaptation matrix.

[0011] Optionally, and in conjunction with the historical content dissemination attenuation factor of each channel, the following steps are taken: obtaining the core theme of the compliant marketing content entity; querying the interaction rate curve of marketing content similar to the core theme in the historical database on each channel over time, and fitting it to obtain the historical content dissemination attenuation factor; and weighting and fusing the calculated cosine similarity with the historical content dissemination attenuation factor to obtain the final matching score.

[0012] Optionally, the intelligent delivery module includes: a channel filtering unit, used to filter out channels with matching scores higher than a preset dynamic threshold based on the channel adaptability matrix, to obtain a delivery channel sequence; a delivery matching unit, used to perform serialized delivery matching between the compliant marketing content entity and the delivery channel sequence, to obtain delivery results; and an interaction acquisition unit, used to perform multi-source signal acquisition and channel identifier merging based on the delivery results, to generate customer interaction data.

[0013] Optionally, the feedback update module includes: a positive conversion unit, used to monitor deep interaction behaviors in the customer interaction data, record the channels and corresponding content fragments where the deep interaction behaviors occur, and generate conversion behavior features; a negative feedback unit, used to monitor negative feedback behaviors in the customer interaction data, record the channels and triggering content fragments where the negative feedback behaviors occur, and generate negative feedback features; and a sample labeling unit, used to label the conversion behavior features as positive samples and the negative feedback features as negative samples, together with the corresponding channel and content features, as feedback data.

[0014] Optionally, the system further includes: grouping positive and negative samples in the feedback data according to channel source, extracting common high-frequency labels and mutually exclusive labels in each group, and generating a profile correction vector; weightedly fusing the profile correction vector with the corresponding channel profile features in the target profile feature library to obtain an updated channel profile sub-library, and writing it back to the target profile feature library.

[0015] Based on the same inventive concept, this invention also provides a method for omnichannel AI-automated customer acquisition in the finance and tax industry based on AIGC technology. The method includes: acquiring publicly available enterprise operating data and a preset customer tagging system in the finance and tax industry; constructing an initial profile feature library; collecting customer behavior data from multiple online channels in real time; dynamically updating the initial profile feature library to generate a target profile feature library; extracting content preference features of the target customer group based on the target profile feature library; analyzing interactive hotspots in the customer behavior data in real time to generate real-time hotspot features; integrating the content preference features and the real-time hotspot features to generate content creation guidance parameters; and inputting the content creation guidance parameters into preset AIGC content. A model is generated to drive the AIGC content generation model to output preliminary marketing content. This preliminary marketing content undergoes standardization verification and professional terminology enhancement to generate compliant marketing content entities. Based on the target profile feature library, the dissemination characteristics and audience matching degree of the compliant marketing content entities on different online channels are analyzed to generate a channel adaptability matrix. According to the channel adaptability matrix, a distribution channel sequence is allocated for the compliant marketing content entities, and cross-channel automated delivery is executed according to the distribution channel sequence to obtain customer interaction data. Conversion behavior features and negative feedback features are extracted from the customer interaction data and input as feedback data into the target profile feature library, initiating an update to the target profile feature library.

[0016] Compared with the prior art, the present invention has the following advantages: 1. This invention achieves precise targeting of potential customers in the finance and tax industry by constructing a dynamically updated target profile feature library and utilizing AIGC technology to generate marketing content highly matched to the profile. It abandons the traditional, crude customer segmentation method based on static tags, and instead, through real-time collection and analysis of online customer behavior data, enables customer profiles to dynamically reflect subtle changes in their focus and emotional inclinations. This, in turn, drives immediate adjustments to content generation and delivery strategies, improving the relevance of marketing information and customer reception.

[0017] 2. This invention establishes a fully automated workflow encompassing customer insight, content creation, channel distribution, and performance feedback, thereby improving the overall operational efficiency of marketing in the finance and tax industry. Through automated data collection, feature extraction, content generation, and cross-channel delivery, marketers are freed from tedious, repetitive tasks, allowing them to focus on higher-level strategic planning. This end-to-end automation capability enables large-scale, personalized marketing campaigns to be executed cost-effectively and efficiently, solving the problem of high labor and time costs inherent in traditional models.

[0018] 3. This invention achieves intelligent allocation and coordination of multi-channel resources by constructing a channel adaptability matrix and combining it with historical content dissemination attenuation factors. It not only assesses the static match between content and channel audiences but also proactively considers the content's dissemination vitality across different channels, thereby selecting the optimal sequence of distribution channels for compliant marketing content entities. This multi-dimensional, data-driven channel selection approach optimizes the synergistic effect of cross-channel marketing activities, reduces resource waste, and ensures that marketing information reaches target customers at the most appropriate time through the most effective channels.

[0019] Other features and advantages of the invention will be set forth in the description which follows, and will be apparent in part from the description, or may be learned by practicing the invention. The objects and other advantages of the invention may be realized and obtained by means of the structures pointed out in the description, claims and drawings. Attached Figure Description

[0020] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0021] Figure 1 This is a framework diagram of an AI-automated customer acquisition system for the finance and tax industry based on AIGC technology, according to an embodiment of the present invention.

[0022] Figure 2 This is a schematic diagram of the structure of an AI-automated customer acquisition system for the finance and tax industry based on AIGC technology, according to an embodiment of the present invention.

[0023] Figure 3 This is a schematic diagram of the channel adaptation matrix according to an embodiment of the present invention.

[0024] Figure 4 This is a flowchart illustrating an AI-based automated customer acquisition method for the finance and tax industry across all channels, according to an embodiment of the present invention. Detailed Implementation

[0025] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0026] Reference Figure 1 One embodiment of the present invention proposes an AI-automated customer acquisition system for the finance and tax industry based on AIGC technology. It adopts a technical approach that combines dynamic profile construction, intelligent content generation, precise channel adaptation, and feedback closed-loop updates, which can realize the automation and intelligence of the entire customer acquisition process in the finance and tax industry and continuously optimize the accuracy of marketing strategies.

[0027] The system described in this embodiment specifically includes: The profile building module is used to acquire publicly available corporate operating data and a preset customer tag system in the finance and taxation industry, build an initial profile feature library, collect customer behavior data from multiple online channels in real time, dynamically update the initial profile feature library, and generate a target profile feature library. Optionally, the portrait construction module includes: The data acquisition unit is used to periodically crawl public posts, comments, and articles containing financial and tax keywords from social media platforms and industry information websites to obtain raw text data; The behavior analysis unit is used to perform entity recognition and sentiment analysis on the raw text data to generate customer behavior data; The association matching unit is used to associate and match the customer behavior data with the customer tags in the initial profile feature library and calculate the matching degree weight. The profile update unit is used to update the initial profile feature library by adding new topics of interest and sentiment tendencies in the customer behavior data as new feature dimensions based on the matching degree weight, thereby forming the target profile feature library.

[0028] Specifically, such as Figure 2 As shown, the social media platforms selected by the data collection unit cover communities frequented by tax professionals and corporate decision-makers, while industry information websites primarily consist of the official websites of national tax authorities and authoritative financial information portals. A pre-set tax keyword database was established, jointly developed by tax experts based on current regulations and market trends, containing 500 core terms such as "tax reduction for small-scale taxpayers" and "corporate income tax settlement." Every 4 hours, a request is automatically sent to the platform to retrieve public posts, comments, and articles containing these keywords, forming raw text data. This collection of unprocessed text information constitutes the raw text data.

[0029] Entity recognition uses a pre-trained deep learning model to locate entities such as tax policy names and industry terms from unstructured text. The input to this deep learning model is raw text data, and the output is the identified tax policy names, industry terms, and their categories. Its training set consists of historical public posts, comments, and articles labeled with tax entity categories. Sentiment analysis determines the text's emotional tendency, outputting a continuous sentiment score ranging from -1.0 to +1.0, where -1.0 represents extreme negativity, +1.0 represents extreme positivity, and 0 represents neutrality. After processing, the jumbled text is transformed into structured customer behavior data, clearly recording users, time, topics, and sentiment intensity.

[0030] The initial profile feature library is a pre-built customer information framework, which includes a customer tag system based on research and analysis of 2,000 existing customers, such as dimensions like "company size" and "areas of interest." The strength of the association is quantified by mapping entities to tags, such as mapping "R&D expense deduction" to "tax incentives," and calculating matching weights. This is used to calculate the matching weight of a specific customer u to a specific customer tag t. ,have: ; in, This is a constant adjustment coefficient, set to 1.2 here. This value was selected based on testing the update and iteration effects of 100 sets of customer profiles. The frequency count of customer u's actions related to tag t within the set evaluation period; The average sentiment score expressed by customer u for content related to tag t during the evaluation period is defined as [-1.0, 1.0].

[0031] A dynamic update threshold was set. When a customer's match weight for a certain tag exceeds this threshold, the customer is considered to have shown strong new interest. If a topic of interest in the behavioral data does not exist in the existing profile, it is added as a new feature dimension to the profile and labeled with sentiment. For example, a customer who originally only focused on "tax compliance" but frequently discusses "green taxation" positively with the required weight will have "green taxation" added to their profile. Through multiple iterations, the initial profile feature library evolved into a target profile feature library that better reflects the customer's current true state.

[0032] For example, suppose the initial profile feature database contains a customer "a certain technology company," whose profile includes customer tags: {Industry: "Information Technology," Company Size: "Startup," Existing Area of ​​Interest: "Software Product Value-Added Tax"}. Through an application programming interface (API), a post from the official account of this "certain technology company" is retrieved from a tax and finance forum. The post reads: "The recent interpretation of the new policy regarding R&D expense deduction for high-tech enterprises is very timely, a major benefit for companies like ours with large R&D investments!" This text constitutes a piece of raw text data. Next, this raw text data is processed. The entity recognition module identifies two entities: "high-tech enterprise" and "R&D expense deduction." Analyzing the phrases "very timely" and "major benefit" in the text, a comprehensive sentiment score of 0.85 is given. After processing, a customer behavior data entry is generated: {Customer ID: "a certain technology company," Entity of Interest: "R&D expense deduction," Sentiment Score: 0.85, Time: "XX-11-01"}. Subsequently, association matching and weight calculation are performed. It was found that "R&D expense deduction" is a topic not found in the "existing areas of interest" of the current profile of "a certain technology company" (u), and therefore it was identified as a new topic of interest. If this is the 5th time "a certain technology company" has mentioned this topic in the most recent evaluation period, and the average sentiment score of these mentions is 0.8, then the matching degree weight is calculated according to the formula. The score exceeded the update threshold of 1.5, triggering the profile update mechanism. The new topic of interest, "R&D expense deduction," and its corresponding sentiment tendency (average sentiment score of 0.8) were added as new feature dimensions to the profile of "A Certain Technology Company." Ultimately, in the target profile feature library, the profile of "A Certain Technology Company" was updated to: {Industry: "Information Technology," Company Size: "Startup," Existing Area of ​​Interest: "Software Product VAT," New Feature Dimension: R&D Expense Deduction: 0.8}. This updated profile more accurately reflects the customer's current focus and attitude.

[0033] The preference hotspot module is used to extract content preference features of the target customer group based on the target profile feature library, and analyze the interaction hotspots in the customer behavior data in real time to generate real-time hotspot features. The content preference features and the real-time hotspot features are then integrated to generate content creation guidance parameters. Optionally, the preference hotspot module includes: The template generation unit is used to parse the structured elements of historical content that matches the preset interaction rate from the content preference features and generate a content template skeleton; The hotspot extraction unit is used to extract fiscal and tax policy keywords and frequently asked phrases that appear beyond a preset frequency within the current time period from the real-time hotspot features, and generate a set of hotspot keywords. The parameter encapsulation unit is used to combine the content template skeleton with the set of hot keywords, and add the content tone and style requirements obtained from the target profile feature library to encapsulate them into content creation guidance parameters.

[0034] Specifically, the target profile feature library aggregates a large number of customer topics of interest and sentiment tendencies. By performing cluster analysis on this data, target customer groups with similar preferences can be identified. The content preference characteristics of the target customer group are a quantitative description of the common preferences shown by these groups in their historical content consumption. To extract a reusable content framework, the performance of each piece of content in the historical content database is analyzed, and its interaction rate is calculated. For calculating the interaction rate of the i-th historical piece of content... ,have: in, The number of likes for the i-th post; Let i be the number of comments on the i-th article; Let i be the number of shares of the i-th piece of content; The number of views or impressions of the i-th piece of content; and These are weighting coefficients used to adjust the importance of different interactive behaviors. Based on regression analysis of 500 published articles, commenting and sharing behaviors contributed more to the final conversion than liking, and can be set... It is 1.5. Version 2.0. A preset interaction rate threshold is set, which is defined as the top 15% of historical content interaction rates. Content exceeding this threshold is considered high-quality content. Natural language processing technology is used to automatically deconstruct the structure of these high-quality contents, identifying high-frequency structured elements such as "opening with a question" and "using case studies for argumentation," which are then combined and sorted to form a content template skeleton. Simultaneously, customer behavior data is analyzed in real-time to capture market trends and generate real-time trending features. Real-time trending features are a collection of topics whose customer discussion has rapidly increased within the current time period. The frequency of occurrence of tax policy keywords and frequently asked phrases is continuously monitored. To determine whether a keyword becomes a trending topic, its frequency of occurrence within the current time period is calculated. The frequency of occurrence of keyword k within the time window Δt is calculated. ,have: ; in, This represents the number of times keyword k is mentioned within the time window Δt; This represents the total number of times all finance and tax-related keywords are mentioned within the same time window. If the frequency in the current time period, such as the past 6 hours, is greater than 2.5 times the average frequency over the past 30 days, the keyword is considered to have abnormal popularity. All finance and tax policy keywords and frequently asked phrases that meet this condition are filtered out to form a set of hot keywords.

[0035] The content template framework, representing long-term preferences, is organically combined with a set of trending keywords representing short-term hot topics. The target demographic feature library is then analyzed again to refine the content tone and style requirements. For example, when targeting corporate executives, the style leans towards "professional, rigorous, and forward-looking"; when targeting startup owners, it leans towards "accessible, practical, and motivating." Ultimately, all elements are encapsulated into a structured data object format as content creation guidance parameters.

[0036] For example, if the target profile feature database shows that a target customer group consisting of "high-tech startups" shows consistently high attention and positive sentiment towards the topic of "R&D expense deduction," then analyzing historical content reveals that an article about "how startups can use tax incentives to accelerate growth" had an interaction rate of 9%, far exceeding the set threshold of 6% for the top 15% interaction rate. After analyzing this article, the identified structured elements are "pain point introduction," "policy interpretation + case study," "clarification of common misconceptions," and "call to action conclusion." These elements combine to generate a content template skeleton: {Paragraph 1: Pain point introduction, Paragraph 2: Policy explanation, Paragraph 3: Case analysis, Paragraph 4: Avoidance guide, Paragraph 5: Call to action}. Simultaneously, real-time analysis of customer behavior data reveals that the common phrase "corporate income tax settlement" appeared 0.05 times in the past 6 hours, 3.33 times its average frequency of 0.015 over the past 30 days, exceeding the preset frequency standard of 2.5 times. Therefore, "corporate income tax settlement" is included in the hot keyword set. Meanwhile, the previously known core topic "R&D expense deduction" is also naturally included, forming a set of hot keywords: {"R&D expense deduction", "corporate income tax settlement"}. Finally, based on the positive emotional tendencies of this group and the attributes of startups in the target profile feature library, the content tone and style requirements are determined as follows: "The language style should be concise and clear, the tone should be positive and encourage innovation." These three parts are encapsulated into the final content creation guidance parameters, the data structure of which may be as follows: {Content template skeleton: ["Pain point introduction", "Policy explanation", "Case analysis", "Pitfall avoidance guide", "Call to action"], Hot keyword set: ["R&D expense deduction", "corporate income tax settlement"], Content tone and style requirements: {Tone: "positive and clear", Style: "concise and clear", Target audience: "startups"}} This content creation guidance parameter fully integrates long-term content preferences and real-time interactive hot topics, providing a precise blueprint for the subsequent generation of highly customized marketing content.

[0037] The content generation module is used to input the content creation guidance parameters into a preset AIGC content generation model, drive the AIGC content generation model to output preliminary marketing content, and perform standardization verification and professional terminology enhancement on the preliminary marketing content to generate compliant marketing content entities. Optionally, the content generation module includes: The hierarchical generation unit is used to decompose the content creation guidance parameters into structural constraints, keyword constraints, and tone constraints, and input them into different generation layers of the AIGC content generation model to generate preliminary marketing content layer by layer. The compliance enhancement unit is used to perform a financial and tax terminology consistency check on the preliminary marketing content, bind the policy-related statements to the corresponding legal sources, and generate enhanced preliminary marketing content. The filtering and splitting unit is used to split the enhanced preliminary marketing content into content fragments, perform compliance screening and negative word filtering, and generate compliant marketing content entities.

[0038] Specifically, the process begins with parsing the received content creation guidance parameters. These parameters are broken down into three independent sets of constraints: structural constraints, keyword constraints, and tone constraints. Structural constraints originate from the content template skeleton, defining the macro-framework and paragraph functions of the final content. Keyword constraints come from a set of trending keywords, specifying the core themes that the content must include. Tone constraints stem from content tone and style requirements, setting a specific stylistic tone for the content's word choice and sentence structure. These constraints are then input into different generation layers of the pre-defined AIGC content generation model. The AIGC content generation model uses a pre-trained language model based on the Transformer decoder architecture as its foundation. Specifically, it comprises 12 decoder layers, each with 12 attention heads, and a feedforward network with 3072 dimensions. This model has been fine-tuned in the field of finance and taxation. Its training data includes policy interpretations, original texts of regulations, and official Q&As published by authoritative institutions such as the State Taxation Administration website and the Accounting Department of the Ministry of Finance, as well as high-quality marketing copy, customer success stories, and frequently asked questions reviewed by finance and tax experts, totaling approximately 500,000 documents. Supervised fine-tuning was used, with differences calculated using the cross-entropy loss function and the AdamW optimizer employed. The initial learning rate was [not specified in the original text]. The batch size is 8, and the training run consists of 5 epochs. Different generation layers of the model handle different levels of generation tasks. For example, the high-level planning layer receives structural constraints to construct the overall logical flow of the content, while the vocabulary selection and syntax organization layer closely references keyword and intonation constraints during the generation process. Through this hierarchical, step-by-step constraint application, the model can generate preliminary marketing content that is both structured and rich in content. After the preliminary marketing content is generated, it enters a dual enhancement and verification phase. The first step is to perform a consistency verification of tax and financial terminology. A built-in authoritative terminology knowledge base containing over 10,000 entries, reviewed by tax and financial experts, is used. The verification program traverses all the professional terms in the preliminary marketing content and compares them with the knowledge base to ensure accuracy. For example, it ensures that "value-added tax special invoice" is not mistakenly written as "value-added tax special ticket". The consistency score for this tax and financial terminology is calculated accordingly. ,have: ; in, Let y be the y-th financial and tax term identified in the initial marketing content; For an indicator function, when the term Its value is 1 if it is not in the knowledge base, and 0 otherwise; The total number of tax and financial terms identified in the initial marketing content; This represents the total number of times tax and financial terms appear in the content. If the passing threshold is set at 0.99, content scoring below this will be flagged and the system will attempt to automatically correct it or submit it for manual review. Simultaneously, all policy-related statements in the content will be linked to their corresponding legal sources. Through semantic recognition technology, sentences mentioning specific tax rates, tax reduction conditions, and other policy information can be located, and the internal legal database will be automatically queried to append the relevant legal provisions as citations or annotations, enhancing the professionalism and credibility of the content. After completing these two operations, the enhanced initial marketing content is obtained.

[0039] Based on semantic coherence and logical integrity, long copy is broken down into independent paragraphs or key points suitable for posting on different social media platforms. Each segment then undergoes compliance screening and negative word filtering: compliance screening is based on a negative word database of relevant advertising laws and industry regulations, eliminating exaggerated claims such as "most cost-effective," promises such as "guaranteed pass," or illegal misleading phrases; negative word filtering corrects unnecessary negative expressions that may cause ambiguity or negative associations. After this purification process, a structured, compliant marketing content entity is finally produced, consisting of multiple rigorously reviewed content segments.

[0040] For example, following the output of the previous steps, the content creation guidance parameters received by the AIGC content generation model are: {Content template skeleton: [“Pain Point Introduction”, “Policy Explanation”, “Case Analysis”, “Pitfall Avoidance Guide”, “Call to Action”], Hot keyword set: [“R&D Expense Deduction”, “Corporate Income Tax Settlement”], Content tone and style requirements: {Tone: “Positive”, Style: “Concise and Clear”}}. Based on this, the AIGC content generation model generates preliminary marketing content: “Is your startup struggling with R&D costs? Don’t worry, the latest R&D expense deduction policy can effectively reduce your tax burden. When conducting corporate income tax settlement, including R&D investment in costs is a money-saving trick every business owner must learn…” Next, this preliminary marketing content undergoes enhanced validation. The tax terminology consistency verification program scans the text and identifies 5 terms, including “R&D Expense Deduction” and “Corporate Income Tax Settlement.” After consulting an authoritative terminology knowledge base, it is confirmed that all terms are used correctly, appearing a total of 5 times. No inconsistent terms were found. Therefore… The verification was successful. The policy statement "the latest R&D expense deduction policy" was recognized, automatically linked to the relevant regulations, and the text was updated to: "The latest R&D expense deduction policy (based on the 'XX Announcement') can effectively reduce your tax burden." This generated the enhanced initial marketing content.

[0041] The enhanced initial marketing content was broken down into five content fragments, each corresponding to one of the five parts of the content template skeleton. During compliance screening, the phrase "money-saving tricks" was identified as misleading and exaggerated, and corrected to "important tax-saving methods." Simultaneously, the negative word filtering program detected the expression "don't worry," deeming it suitable to be replaced with the more positive "good news." After processing, a compliant marketing content entity was finally generated, with the following structured data: {Fragment 1: "Good news! Is your startup struggling with R&D costs? The latest R&D expense deduction policy...", Fragment 2: "...", ...}. Each fragment in this entity is now cleaned up and compliant with regulatory requirements.

[0042] The channel adaptation module is used to analyze the dissemination characteristics and audience matching degree of the compliant marketing content entity on different online channels based on the target profile feature library, and generate a channel adaptation degree matrix. Optionally, the channel adaptation module includes: The channel profiling unit is used to extract the profile features of the historical audience of each online channel from the target profile feature library and generate a channel audience feature vector. The content feature unit is used to extract features from the compliant marketing content entity and generate a content feature vector. The scoring and matching unit is used to calculate the cosine similarity between the content feature vector and the audience feature vector of each channel, and combine it with the historical content dissemination attenuation factor of each channel to obtain the matching score of each channel, thus forming a channel matching matrix.

[0043] Specifically, it requires profiling the audience of each candidate online channel. This involves extracting data from a target profile feature library and categorizing the data according to its source channel. For each online channel, it aggregates the profile features of all users active on that channel, such as user industry distribution, company size, and tax-related topics of interest. Through weighted averaging and min-max normalization, it calculates the weighted average for a specific feature dimension d in channel j, such as "interest in tax incentives." ,have: ; in, For the set of users active on channel j; This represents the original value of user u in feature dimension d; The weight of user u can be determined by factors such as activity level and interaction frequency, for example... These discrete and continuous features are transformed into a multi-dimensional numerical vector, namely the channel audience feature vector. Each dimension of this vector represents a specific attribute of the audience group. For example, the first dimension might represent the average level of attention paid to the topic of "tax incentives," while the second dimension might represent the proportion of "senior managers" among the audience. In this way, each online channel has a unique channel audience feature vector that represents the overall profile of its user group.

[0044] Three core dimensions were extracted: text complexity (quantified by average sentence length, proportion of long and difficult words, etc.), topic professionalism (keyword density of authoritative financial and tax terminology), and presentation format (classification variable encoding: 1 for plain text short articles, 2 for text and image long articles, and 3 for articles containing video links). The quantitative values ​​of these three dimensions together constitute a three-dimensional content feature vector.

[0045] Once we have numerical vectors representing content and channels, we can use cosine similarity to measure the matching degree between the content feature vector and the audience feature vector of each channel. For calculating content entities... With channels audience Cosine similarity between ,have: ; in, Content feature vectors for compliant marketing content entities; For channels The channel audience characteristic vector is used. Considering the dynamic dissemination pattern, a historical content dissemination decay factor is also added. The core theme of the current content, such as "R&D expense deduction," is identified. A historical content database is queried to analyze the interaction rate of content on the same theme across various channels over time, fitting a dissemination decay curve and extracting the decay factor. This factor reflects the vitality of content on a specific channel; the closer the value is to 1, the slower the decay.

[0046] The cosine similarity score is weighted and fused with the historical content dissemination attenuation factor to obtain the final matching score for each channel. The scores from all channels are then aggregated to form a channel fit matrix. For example... Figure 3 As shown, the rows of the matrix represent different "compliant marketing content entities", and the columns represent different "online channels". The color depth and value of the cells represent the matching score of specific content with specific channels, with darker colors indicating higher matching degrees.

[0047] For example, if the compliant marketing content entity is a long article with images about "R&D expense deduction," and the target audience is high-tech startups, the first step is to extract multi-dimensional features from this content entity to obtain a content feature vector. If its text complexity score is 7.5, its topic professionalism score is 9.0, and its representation encoding is 2, then the content feature vector... Next, we analyze two alternative channels. Channel A is a community for finance and tax professionals, and Channel B is a comprehensive forum for entrepreneurs. After extracting data from the target profile feature library, we obtain the channel audience feature vector for Channel A that perfectly corresponds to the content feature vector dimension. Next, we analyze two alternative channels. Channel A is a community for finance and tax professionals, and Channel B is a comprehensive forum for entrepreneurs. After extracting data from the target profile feature library, we obtain the channel audience feature vector for Channel A that perfectly corresponds to the content feature vector dimension. This indicates that its audience is highly professional and prefers in-depth text and image content. The channel audience feature vector for Channel B is... This indicates that its audience has a moderate level of professionalism and prefers short, plain text content. The cosine similarity between the content and each channel was calculated: Similarly, we can calculate... To simplify calculations, the vector dimensions are pre-aligned. Next, the historical content dissemination decay factor is analyzed. Historical data shows that professional topics like "R&D expense deduction" generate long-term discussion on channel A with slow decay, resulting in a historical content dissemination decay factor of 0.95. On channel B, however, the information feed refreshes quickly, and the attention given to similar content declines rapidly after 48 hours, resulting in a historical content dissemination decay factor of 0.50. Finally, the final matching score is calculated to form a channel suitability matrix.

[0048] Optionally, and in conjunction with historical content dissemination attenuation factors for each channel, the following factors are considered: Obtain the core theme of the compliant marketing content entity; By querying the historical database and observing the interaction rate curves of marketing content similar to the core theme across various channels over time, the historical content dissemination decay factor can be obtained through fitting. The calculated cosine similarity is weighted and fused with the historical content propagation attenuation factor to obtain the final matching score.

[0049] Specifically, the process begins by identifying the core theme of the current compliant marketing content entity. By analyzing the content text and combining it with a set of trending keywords, keywords with the highest weight or those in a central position, such as "R&D expense deduction," are typically designated as the core theme.

[0050] After identifying the core theme, use it as an index to query the internal historical database. The database stores all past marketing content placement records across various channels and detailed interaction data over time. Filter out historical content similar to the current core theme, and extract complete data sequences of interaction rates over time for each online channel after content publication. The interaction rate is recorded hourly, using the formula mentioned earlier, recording the interaction rate for hours 1, 2, 3… up to 168 hours after publication, forming discrete data points. To obtain stable quantitative indicators, an exponential decay model is used for curve fitting. The online spread of content typically follows an exponential decay law; the model expression is: ; in, The time after the content was published, in hours; For the time point after the content is published Theoretical interaction rate; The initial peak interaction rate of the content; To fit the specific channel The average decay constant for this core theme, expressed as the reciprocal of the hour, is calculated using least squares regression analysis on multiple historical content data points. After obtaining the decay constant, it needs to be transformed into a standardized, easily fused dimensionless factor—the historical content dissemination decay factor. This involves calculating the historical content dissemination decay factor for channel j targeting the current core theme. ,have: ; in, This is a unit time constant, set to 1 hour here. The final step involves weighting and fusing this newly calculated historical content propagation attenuation factor with the previously calculated cosine similarity to correct and refine the matching evaluation, resulting in the final matching score. (For calculation channels...) Final Match Rating ,have: ; in, These are weighting coefficients, derived from experimental calibration.

[0051] For example, the core theme of the compliant marketing content entity was identified as "R&D expense deduction". The system queries the historical database for marketing content with the same theme as "R&D expense deduction". For Channel A, a community of finance and tax professionals, the query results show the interaction rate data for five historical articles on this topic. The data shows that these articles maintained their popularity; 72 hours after publication, the average interaction rate remained at about half of its peak. The system inputs these data points into an exponential decay model for fitting, calculating the average decay constant for Channel A. This indicates that the interaction rate decreases by approximately 1% per hour. For Channel B, a comprehensive entrepreneur forum, the query results show data from 10 historical articles on related topics. The data shows that these articles had high interaction rates initially, but the popularity faded rapidly; after 24 hours, the average interaction rate had dropped to below 10% of its peak. By fitting these data points, the average decay constant for Channel B can be calculated. This indicates that the interaction rate decreases by approximately 10% per hour. Next, the calculated decay constant is converted into a historical content dissemination decay factor, with the unit time constant set to 1 hour: the historical content dissemination decay factor for channel A is... The historical content dissemination attenuation factor for channel B is: If the cosine similarity between the content and channel A is 0.98, and the cosine similarity with channel B is 0.65, the weighting coefficient... If the score is 0.7, then the final matching score for channel A is... Final matching score for Channel B Through this series of detailed calculations, the final matching score was obtained, providing crucial dynamic data support for the generation of the channel fit matrix.

[0052] The intelligent delivery module is used to allocate delivery channel sequences for the compliant marketing content entity according to the channel compatibility matrix, and to perform cross-channel automated delivery according to the delivery channel sequence to obtain customer interaction data. Optionally, the intelligent delivery module includes: The channel filtering unit is used to filter out channels with matching scores higher than a preset dynamic threshold based on the channel adaptability matrix, thereby obtaining a delivery channel sequence. The delivery matching unit is used to perform serialized delivery matching between the compliant marketing content entity and the delivery channel sequence to obtain the delivery result; The interactive acquisition unit is used to acquire multi-source signals and merge channel identifiers based on the delivery results to generate customer interaction data.

[0053] Specifically, firstly, all channels that meet the matching score criteria are filtered from the matrix based on a dynamic threshold. This dynamic threshold is adaptively adjusted based on the overall ROI of recent marketing campaigns. The calculation of this dynamic threshold... ,have: in, This is a base threshold set at 0.75. This value is based on data analysis of over 500 historical campaigns. The analysis shows that channels with a match score higher than 0.75 have a greater than 90% probability of generating a positive return on investment. It represents the percentage change in the average return on investment across all marketing channels within the most recent evaluation period, such as the previous quarter. This is an adjustment coefficient, usually set to 0.5. Channels with a matching score not lower than the dynamic threshold are selected and sorted from highest to lowest score to form an ordered delivery channel sequence. Serialized delivery matching then occurs: instead of simultaneously delivering to all channels, it executes according to the sequence order and the optimal release time window for each channel. The automated delivery engine calls the application programming interfaces (APIs) of each channel to accurately release content fragments in a preset format at preset times. For example, the channel with the highest score prioritizes delivering core content fragments, while the channel with the next highest score delivers supplementary or lead-generating content. All execution records, such as channel, content fragment, and delivery time, constitute the delivery result. After delivery, the data collection program is immediately started to collect multi-source signals and merge channel identifiers. Interaction data from each channel, including impressions, reads, and likes, is continuously acquired through the application programming interface. To build a complete user behavior chain, channel identifier merging is performed: when the same user interacts with marketing content through different channels, such as reading an article on channel A and then accessing the official website through a link on channel B, the scattered behavioral signals are associated and merged under a unique customer identifier using tracking parameters or cross-platform user identification technology. After merging, the original scattered interaction signals are integrated into customer interaction data with a clear structure and customer-centric focus, providing a foundation for subsequent effect evaluation and profile iteration.

[0054] For example, the generated channel fit matrix is: {Channel A: 0.971, Channel B: 0.605}. To make the example more complete, if there is also a Channel C, namely a social media platform in an emerging industry, whose final matching score is 0.810, then first, we need to determine the dynamic threshold used for filtering. The base threshold is 0.75. Assume that the overall ROI of the marketing campaign in the previous quarter increased by 8% compared to the previous quarter. The matching scores of all channels were compared with 0.78. Channel A: 0.971 ≥ 0.78, selected. Channel C: 0.810 ≥ 0.78, selected. Channel B: 0.605 < 0.78, not selected. The selected channels were sorted by score to obtain the channel sequence: [Channel A, Channel C]. Next, sequential matching was performed. The compliant marketing content entity was a long article about "R&D expense deduction," which had been broken down into multiple content segments. First, on Channel A, which ranked first, at 10 AM on Tuesday, when its users were most active, the core content segment containing in-depth analysis and case studies was delivered. Then, according to the sequence, on Channel C, which ranked second, at 8 PM on Wednesday, when its users were in their evening leisure time, a more concise summary content segment with visual charts was delivered, along with a link to the original article on Channel A. The delivery results generated during this process are recorded as follows: {Delivery Task 1:{Channel:“Channel A”, Content Segment ID:[“Segment 1”,“Segment 2”], Time:“XX-11-14 10:00”}, Delivery Task 2:{Channel:“Channel C”, Content Segment ID:[“Segment 3”], Time:“XX-11-15 20:00”}}. After delivery, data collection begins. It retrieves data from the API of Channel A: 10,000 reads, 250 likes. It retrieves data from the API of Channel C: 50,000 impressions, 1,200 likes, 80 link clicks. A client, “Mr. Zhang”, first read the entire article on Channel A, then saw the summary on Channel C, and clicked the link at the end of the article to jump to the official website. The tracking parameters in the link identified that both actions originated from “Mr. Zhang”, and the channel identifiers were merged. Ultimately, the generated customer interaction data will contain the following records: {Customer ID: "ZhangZong_Unified", Behavior: "In-depth reading", Channel: "Channel A", Content fragment ID: "Fragment 1", Timestamp: "..."}, {Customer ID: "ZhangZong_Unified", Behavior: "Clicked the official website link", Channel: "Channel C", Content fragment ID: "Fragment 3", Timestamp: "..."}.

[0055] The feedback update module is used to extract conversion behavior features and negative feedback features from the customer interaction data, and input them as feedback data into the target profile feature library to initiate the update of the target profile feature library.

[0056] Optionally, the feedback update module includes: The positive conversion unit is used to monitor deep interaction behaviors in the customer interaction data, record the channels and corresponding content fragments where the deep interaction behaviors occur, and generate conversion behavior characteristics. The negative feedback unit is used to monitor negative feedback behavior in the customer interaction data, record the channels and triggering content fragments of the negative feedback behavior, and generate negative feedback features. The sample labeling unit is used to label the conversion behavior features as positive samples and the negative feedback features as negative samples, together with the corresponding channel and content features, as feedback data.

[0057] Specifically, the process first defines deep interaction behaviors, which go beyond superficial interactions like browsing and liking, and clearly indicate strong customer interest. Based on behavioral path analysis of 1000 successful conversion cases, quantitative standards are preset, such as "spending more than 150% of the average reading time on the content page," "watching the complete introductory video," and "downloading the attached white paper." Whenever these behaviors are detected, contextual information such as customer identifiers, channels, and content fragments is recorded and integrated into structured conversion behavior characteristics. Alongside positive signals, negative feedback behaviors are also closely monitored, i.e., behaviors in which customers explicitly express disinterest or aversion. Negative feedback is divided into direct types, such as clicking "not interested" and negative comments, and indirect types, such as closing the page within a short time and repeatedly skipping similar content. A negative behavior score model is set up to quantify the intensity of these behaviors, and this is used to calculate the overall negative feedback score. ,have: ; in, This is the weight of the m-th type of negative feedback behavior, which is set manually based on its commercial impact. For example, the weight of "unfollow" is set to 0.8, which is higher than the weight of "quick close" which is set to 0.3. It is the frequency with which the user performs the m-th type of negative feedback behavior during the evaluation period; This represents the total number of all predefined negative feedback behavior types. When a customer's overall negative feedback score for a content segment exceeds a preset threshold, such as 1.0, it is considered significant negative feedback. The relevant channels and content segments are recorded and integrated into negative feedback features. After extracting conversion behavior features and negative feedback features, they are transformed into a format suitable for model learning. Each conversion behavior feature is labeled as a positive sample, representing successful reach and interest stimulation; each negative feedback feature is labeled as a negative sample, revealing a mismatch. Simultaneously, the associated contextual information, delivery channel features, and content features are packaged together. The final output feedback data is a structured dataset containing triples of {sample label (positive / negative), channel feature vector, and content feature vector}.

[0058] For example, using the above-mentioned delivery scenario, the content has been delivered to channels A and C. When monitoring customer interaction data, it was found that customer "ZhangZong_Unified" spent 5 minutes on the page after reading a core content segment about "R&D expense deduction" on channel A, far exceeding the average reading time of 2 minutes by 150% (3 minutes). This was determined to be a deep interaction. This event was immediately recorded, generating a conversion behavior feature: {Customer ID: "ZhangZong_Unified", Behavior: "Extremely Long Dwell Time", Channel: "Channel A", Content Segment ID: "Segment 1"}. Simultaneously, it was detected that another customer, "Li_Corp_CFO", clicked the "Reduce Such Recommendations" button provided by the platform after seeing the summary content segment on channel C. This was a clear and direct negative feedback behavior. Assuming this behavior has a weight of 0.6 and a frequency of 1 time, the negative feedback score... Although the single action did not exceed the threshold of 1.0, assuming the customer subsequently quickly closed another related push notification, the score increased by 0.3, reaching a cumulative total of 0.9. To simplify the example, assume the strategy is to directly record any direct negative feedback action. Thus, a negative feedback feature is generated: {Customer ID: "Li_Corp_CFO", Action: "Click Not Interested", Channel: "Channel C", Content Fragment ID: "Fragment 3"}. Next, these features are labeled and packaged. The conversion behavior feature of "ZhangZong_Unified" is labeled as a positive sample. The channel audience feature vector of Channel A is retrieved. and the content feature vector of content fragment 1 The final packaged feedback data record is as follows: {Sample Label: 1, Channel Feature Vector: [8.0, 9.2, 2.1], Content Feature Vector: [7.8, 9.5, 2.0]}, where "1" represents a positive sample. Similarly, the negative feedback features of "Li_Corp_CFO" are marked as negative samples. Through this process, each key customer interaction is transformed into structured feedback data with rich contextual information and clear positive and negative labels, providing a direct and quantifiable basis for subsequent profile correction.

[0059] Optionally, the system further includes: The positive and negative samples in the feedback data are grouped according to their channel source, and the common high-frequency tags and mutually exclusive tags in each group are extracted to generate a profile correction vector. The image correction vector is weighted and fused with the corresponding channel image features in the target image feature library to obtain an updated channel image sub-library, which is then written back to the target image feature library.

[0060] Specifically, the first step is to group the feedback data by channel source. Each piece of feedback data includes information about the online channel from which it originated. Based on this, all feedback data is assigned to the corresponding channel group; for example, all feedback data from channel A forms one group, and those from channel B forms another. Within each channel group, the data is further divided into a positive sample set and a negative sample set.

[0061] Here, "tags" refer to the various dimensions that constitute the content feature vector or the channel audience feature vector. To quantify the bias of these tags, the mean vector of the feature vectors associated with all positive samples and the mean vector of the feature vectors associated with all negative samples within each channel are calculated. From the differences in these mean vectors, it is possible to identify which features are popular and which are unpopular. This difference is quantified as a profile correction vector. For calculating the profile correction vector generated for channel j... ,have: ; in, Let be the set of positive samples for channel j; Let be the set of negative samples for channel j; Let be the number of positive samples in channel j; Let be the number of negative samples in channel j; This is an adjustment coefficient, which can be set to 1.0. Based on the convergence test of 100 portrait iterations, setting it to 1.0 can ensure learning efficiency while avoiding over-correction. To represent positive samples and negative samples The corresponding feature vector. After generating the profile correction vector, the final update operation is performed. This profile correction vector is then weighted and fused with the original profile features of the corresponding channel in the target profile feature library. For channel j, the updated channel profile feature vector is... ,have: ; in, For channels Original channel profile feature vector before update; The learning rate is a decimal between 0 and 1, which can be set to 0.1 here. The calculated new vector constitutes the updated channel profile sub-library. This updated sub-library will be written back to the target profile feature library, replacing the original channel profile data. At this point, a complete feedback-driven profile update closed-loop process is completed.

[0062] For example, feedback data was collected from Channel A and Channel C. First, the feedback data was grouped by channel source. Assume that within an evaluation period, Channel A collected 30 feedback items, of which 25 were positive samples and 5 were negative samples. Channel C collected 50 feedback items, of which 10 were positive samples and 40 were negative samples. Focusing on Channel A, the mean vector of the content feature vectors associated with the 25 positive samples was calculated to be [7.9, 9.6, 2.1], indicating that content that succeeded on Channel A was characterized by high text complexity, high topic specialization, and long-form text and image content. Meanwhile, the mean vector of the content feature vectors associated with the 5 negative samples was [4.5, 5.0, 1.2]. Let... Then calculate the image correction vector for channel A. Next, this corrected profile vector will be used to update the original profile features of channel A. If the original channel profile feature vector of channel A is [8.0, 9.2, 2.1], and the learning rate is set to 0.1, then... This constitutes the updated channel profile sub-library for channel A. It will then be written back to the target profile feature library.

[0063] Based on the same inventive concept, such as Figure 4 As shown, this invention also provides a method for omnichannel AI-automated customer acquisition in the finance and tax industry based on AIGC technology, the method comprising: Obtain publicly available corporate operating data and a pre-defined customer tag system from the finance and tax industry to construct an initial profile feature library. Collect customer behavior data from multiple online channels in real time, dynamically update the initial profile feature library, and generate a target profile feature library. Based on the target profile feature library, extract the content preference features of the target customer group, and analyze the interaction hotspots in the customer behavior data in real time to generate real-time hotspot features. Combine the content preference features with the real-time hotspot features to generate content creation guidance parameters. The content creation guidance parameters are input into a preset AIGC content generation model, which drives the AIGC content generation model to output preliminary marketing content. The preliminary marketing content is then subjected to standardization verification and professional terminology enhancement to generate a compliant marketing content entity. Based on the target profile feature library, the dissemination characteristics and audience matching degree of the compliant marketing content entity on different online channels are analyzed to generate a channel adaptability matrix. Based on the channel compatibility matrix, the compliant marketing content entity is assigned a delivery channel sequence, and cross-channel automated delivery is executed according to the delivery channel sequence to obtain customer interaction data; Conversion behavior features and negative feedback features are extracted from the customer interaction data and input as feedback data into the target profile feature library to initiate the update of the target profile feature library.

[0064] It should be noted that the electrical connections between the various units described above do not necessarily represent direct or indirect connections. Any indirect connection method can be applied to the embodiments of the present invention as long as it achieves the purpose of the present invention. The above descriptions are merely exemplary embodiments of the present invention and should not be construed as limiting the scope of the present invention.

[0065] All equivalent changes and modifications made in accordance with the teachings of this invention are still within the scope of this invention. Those skilled in the art will readily conceive of other embodiments of this invention upon considering the specification and the disclosure of practical truth. This application is intended to cover any variations, uses, or adaptations of this invention that follow the general principles of this invention and include common knowledge or conventional techniques in the art not described herein.

Claims

1. A multi-channel AI-automated customer acquisition system for the finance and tax industry based on AIGC technology, characterized in that: The system includes: The profile building module is used to acquire publicly available corporate operating data and a preset customer tag system in the finance and taxation industry, build an initial profile feature library, collect customer behavior data from multiple online channels in real time, dynamically update the initial profile feature library, and generate a target profile feature library. The preference hotspot module is used to extract content preference features of the target customer group based on the target profile feature library, and analyze the interaction hotspots in the customer behavior data in real time to generate real-time hotspot features. The content preference features and the real-time hotspot features are then integrated to generate content creation guidance parameters. The content generation module is used to input the content creation guidance parameters into a preset AIGC content generation model, drive the AIGC content generation model to output preliminary marketing content, and perform standardization verification and professional terminology enhancement on the preliminary marketing content to generate compliant marketing content entities. The channel adaptation module is used to analyze the dissemination characteristics and audience matching degree of the compliant marketing content entity on different online channels based on the target profile feature library, and generate a channel adaptation degree matrix. The intelligent delivery module is used to allocate delivery channel sequences for the compliant marketing content entity according to the channel compatibility matrix, and to perform cross-channel automated delivery according to the delivery channel sequence to obtain customer interaction data. The feedback update module is used to extract conversion behavior features and negative feedback features from the customer interaction data, and input them as feedback data into the target profile feature library to initiate the update of the target profile feature library.

2. The AI-automated customer acquisition system for the finance and tax industry based on AIGC technology according to claim 1, characterized in that, The profile construction module includes: The data acquisition unit is used to periodically crawl public posts, comments, and articles containing financial and tax keywords from social media platforms and industry information websites to obtain raw text data; The behavior analysis unit is used to perform entity recognition and sentiment analysis on the raw text data to generate customer behavior data; The association matching unit is used to associate and match the customer behavior data with the customer tags in the initial profile feature library and calculate the matching degree weight. The profile update unit is used to update the initial profile feature library by adding new topics of interest and sentiment tendencies in the customer behavior data as new feature dimensions based on the matching degree weight, thereby forming the target profile feature library.

3. The AI-automated customer acquisition system for the finance and tax industry based on AIGC technology according to claim 1, characterized in that, The preference hotspot module includes: The template generation unit is used to parse the structured elements of historical content that matches the preset interaction rate from the content preference features and generate a content template skeleton; The hotspot extraction unit is used to extract fiscal and tax policy keywords and frequently asked phrases that appear beyond a preset frequency within the current time period from the real-time hotspot features, and generate a set of hotspot keywords. The parameter encapsulation unit is used to combine the content template skeleton with the set of hot keywords, and add the content tone and style requirements obtained from the target profile feature library to encapsulate them into content creation guidance parameters.

4. The AI-automated customer acquisition system for the finance and tax industry based on AIGC technology according to claim 1, characterized in that, The content generation module includes: The hierarchical generation unit is used to decompose the content creation guidance parameters into structural constraints, keyword constraints, and tone constraints, and input them into different generation layers of the AIGC content generation model to generate preliminary marketing content layer by layer. The compliance enhancement unit is used to perform a financial and tax terminology consistency check on the preliminary marketing content, bind the policy-related statements to the corresponding legal sources, and generate enhanced preliminary marketing content. The filtering and splitting unit is used to split the enhanced preliminary marketing content into content fragments, perform compliance screening and negative word filtering, and generate compliant marketing content entities.

5. The AI-automated customer acquisition system for the finance and tax industry based on AIGC technology according to claim 1, characterized in that, The channel adaptation module includes: The channel profiling unit is used to extract the profile features of the historical audience of each online channel from the target profile feature library and generate a channel audience feature vector. The content feature unit is used to extract features from the compliant marketing content entity and generate a content feature vector. The scoring and matching unit is used to calculate the cosine similarity between the content feature vector and the audience feature vector of each channel, and combine it with the historical content dissemination attenuation factor of each channel to obtain the matching score of each channel, thus forming a channel matching matrix.

6. The omnichannel AI-automated customer acquisition system for the finance and tax industry based on AIGC technology as described in claim 5, characterized in that, In addition, the historical content dissemination attenuation factors from various channels include: Obtain the core theme of the compliant marketing content entity; By querying the historical database and observing the interaction rate curves of marketing content similar to the core theme across various channels over time, the historical content dissemination decay factor can be obtained through fitting. The calculated cosine similarity is weighted and fused with the historical content propagation attenuation factor to obtain the final matching score.

7. The AI-automated customer acquisition system for the finance and tax industry based on AIGC technology according to claim 1, characterized in that, The intelligent delivery module includes: The channel filtering unit is used to filter out channels with matching scores higher than a preset dynamic threshold based on the channel adaptability matrix, thereby obtaining a delivery channel sequence. The delivery matching unit is used to perform serialized delivery matching between the compliant marketing content entity and the delivery channel sequence to obtain the delivery result; The interactive acquisition unit is used to acquire multi-source signals and merge channel identifiers based on the delivery results to generate customer interaction data.

8. The AI-automated customer acquisition system for the finance and tax industry based on AIGC technology according to claim 1, characterized in that, The feedback update module includes: The positive conversion unit is used to monitor deep interaction behaviors in the customer interaction data, record the channels and corresponding content fragments where the deep interaction behaviors occur, and generate conversion behavior characteristics. The negative feedback unit is used to monitor negative feedback behavior in the customer interaction data, record the channels and triggering content fragments of the negative feedback behavior, and generate negative feedback features. The sample labeling unit is used to label the conversion behavior features as positive samples and the negative feedback features as negative samples, together with the corresponding channel and content features, as feedback data.

9. A financial and tax industry omnichannel AI automatic customer acquisition system based on AIGC technology as described in claim 8, characterized in that, The system also includes: The positive and negative samples in the feedback data are grouped according to their channel source, and the common high-frequency tags and mutually exclusive tags in each group are extracted to generate a profile correction vector. The image correction vector is weighted and fused with the corresponding channel image features in the target image feature library to obtain an updated channel image sub-library, which is then written back to the target image feature library.

10. A method for omnichannel AI-automated customer acquisition in the finance and tax industry based on AIGC technology, employing the omnichannel AI-automated customer acquisition system for the finance and tax industry based on AIGC technology as described in any one of claims 1-9, characterized in that... The method includes: Obtain publicly available corporate operating data and a pre-defined customer tag system from the finance and tax industry to construct an initial profile feature library. Collect customer behavior data from multiple online channels in real time, dynamically update the initial profile feature library, and generate a target profile feature library. Based on the target profile feature library, extract the content preference features of the target customer group, and analyze the interaction hotspots in the customer behavior data in real time to generate real-time hotspot features. Combine the content preference features with the real-time hotspot features to generate content creation guidance parameters. The content creation guidance parameters are input into a preset AIGC content generation model, which drives the AIGC content generation model to output preliminary marketing content. The preliminary marketing content is then subjected to standardization verification and professional terminology enhancement to generate a compliant marketing content entity. Based on the target profile feature library, the dissemination characteristics and audience matching degree of the compliant marketing content entity on different online channels are analyzed to generate a channel adaptability matrix. Based on the channel compatibility matrix, the compliant marketing content entity is assigned a delivery channel sequence, and cross-channel automated delivery is executed according to the delivery channel sequence to obtain customer interaction data; Conversion behavior features and negative feedback features are extracted from the customer interaction data and input as feedback data into the target profile feature library to initiate the update of the target profile feature library.

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