A cross-border e-commerce-oriented multilingual content self-adaptive generation and cultural compliance verification system and method

Abstract

The application relates to a multilingual content self-adaptive generation and cultural compliance verification system for cross-border e-commerce, which comprises scene understanding agents, cultural compliance agents, content generation agents and interactive learning agents in sequence. The cultural compliance agents comprise a multi-modal dynamic cultural compliance knowledge base and an arbitration engine with semantic and context double verification capabilities, which are used for identifying explicit and implicit compliance risks in the content and outputting modification suggestions; the content generation agents and the verification engine are deeply cooperative, and can generate or optimize multistyle and multilingual content in real time according to compliance constraints; the interactive learning agents collect market feedback after delivery in a timely manner, and drive the cooperative incremental update of the knowledge base, the verification model and the generation model. Through the cooperation between the multiple agents, an integrated closed-loop process of content generation and compliance verification is realized, and the creation efficiency, cultural compliance and system self-adaptive capability of cross-border e-commerce content are significantly improved.

Description

Technical Field

[0001] This invention relates to the field of cross-border e-commerce content generation technology, and in particular to a system and method for adaptive generation and cultural compliance verification of multilingual content for cross-border e-commerce. Background Technology

[0002] With the rapid development of global e-commerce, merchants need to generate multilingual marketing content, product descriptions, and advertising materials tailored to different countries and regions. However, significant cultural differences, legal regulations, and platform rules exist between different markets. Direct translation or simple editing can easily lead to cultural misunderstandings, value conflicts, and even legal risks. Currently available content generation tools mostly focus on machine translation and basic copywriting optimization, lacking a systematic understanding of the deep cultural context, laws and regulations, and social taboos of the target market. For example, patent CN120805920A discloses a multi-country compliance intelligent auditing method, device, equipment, and medium for cross-border e-commerce. Although it constructs a structured knowledge graph, its knowledge sources mainly rely on the interpretation of legal texts and do not explicitly cover unstructured and dynamically evolving cultural knowledge such as cultural customs, values, symbolic representations, and historical taboos. Furthermore, the semantic analysis model it uses is mainly applied to the structured extraction of legal texts and does not possess cross-modal and cross-cultural contextual reasoning capabilities, such as the implicit meanings of image and text combinations, regional slang, and in-depth interpretations of cultural symbols. It is difficult to cope with cultural risks such as the differences in the meanings of various contents in different countries, which leads to a high rate of misjudgment and underreporting.

[0003] Secondly, the main function of its compliance review system is to review existing trade documents, not to generate content. Although it supports extracting text information from images, its purpose is to generate trade documents for review. It completely lacks a real-time collaborative mechanism between generation and review. Users still need to generate content externally before submitting it for review, resulting in a broken linear process, low iteration efficiency, and the inability of the generation end to predict compliance boundaries in the early stages of creation, causing a lot of rework.

[0004] In summary, existing technical solutions are still in the stage of rule matching with low intelligence and high false positives. They may face long-term problems such as fragmented workflows despite the introduction of AI, lack of deep contextual understanding and continuous evolution capabilities, long iteration cycles, low efficiency, and difficulty in ensuring that the final content is consistent in terms of compliance and marketing effectiveness. Summary of the Invention

[0005] (a) Technical problems to be solved In view of the problems of existing content generation tools, this invention uses a compliance verification and arbitration engine to perform semantic and contextual analysis, and makes intelligent decisions based on confidence level, scenario priority, and preset rules. This allows the system to accurately identify context-related risks, cross-modal cues, and implicit risks such as emerging cultural trends, thereby solving the problems of high false positive and false negative rates in traditional verification. At the same time, the compliance verification and arbitration engine and the content generation agent collaborate deeply and in real time. The content generation agent not only receives compliance constraints from the compliance verification and arbitration engine before content generation, effectively avoiding risks during content generation, but also directly optimizes the content based on the modification opinions of the compliance verification and arbitration engine after generation. This constitutes a multi-agent collaborative system that verifies and optimizes simultaneously during generation, shortening the content generation cycle while ensuring high-quality and compliant content. This solves the problems of long iteration cycles, low efficiency, and the inability to ensure the consistency of compliance and marketing effectiveness in the final content.

[0006] (II) Technical Solution To achieve the above objectives, the present invention provides the following technical solution: A system for adaptive generation and cultural compliance verification of multilingual content for cross-border e-commerce, characterized in that the system consists of multiple AI agents with autonomous decision-making and interaction capabilities and a shared knowledge base, including: (1) Scene understanding agent: It is used to receive input parameters such as the original content, target market, scene tags and multilingual styles of the user, extract relevant content through the intent parsing model, and output a structured intent vector. ; (2) Cultural compliance intelligent agent: including cultural compliance knowledge base and compliance verification and arbitration engine, wherein the cultural compliance knowledge base stores structured cross-cultural compliance rules and multimodal cases in a hierarchical manner; (3) Content generation intelligent agent: It works in real time with the compliance verification decision module to receive the compliance constraint vector and modification suggestions output by the compliance verification decision module, and generate or optimize multilingual content that meets the requirements by combining market tags and target market characteristics; (4) Interactive learning agent: used to transform user feedback, market compliance events and manual review records after content delivery into structured feedback data, and drive the models in the cultural compliance knowledge base, compliance verification and arbitration engine and the scene adaptive generation module to perform multi-module collaborative updates.

[0007] The scene-understanding agent is responsible for receiving and processing multi-dimensional information from user input, including but not limited to original content, target market identifiers, scene tags, and optional language style parameters. The original content supports multiple modalities such as text, images, voice, and video. Specifically, the target market identifier clarifies the country, region, or cultural / linguistic area where the content is targeted; scene tags define the specific business or dissemination scenario of the content, including product detail pages, social media advertising, holiday marketing, and automated customer service replies; language style parameters are further subdivided into dimensions such as formality, emotional tone, and audience affinity.

[0008] Preferably, the cultural compliance knowledge base adopts a hierarchical storage using a graph structure, specifically including rules and cases in the cultural layer, legal layer, and platform layer. The nodes of the graph structure support multimodal representation of text and image feature vectors, and its edge relationships include at least one association type among semantic conflict, substitution, and reinforcement.

[0009] Preferably, the compliance verification and arbitration engine specifically includes: (1) Semantic compliance analysis agent: used for literal review, adopts multilingual pre-trained model and knowledge graph embedding, identifies explicit violation elements in text or images based on the content of cultural compliance knowledge base, and outputs the first risk label set. Its mathematical expression is: in, As a risk label, Confidence level; (2) Contextual Risk Reasoning Agent: Used for additional review, it adopts graph neural network, combines relevant content such as scenario, product category and target user group, analyzes the potential hidden risks of the content, and outputs a second risk label set. Its mathematical expression is: (3) Dynamic Arbitration Center: Receives risk tag sets generated by the semantic compliance unit and the early warning risk reasoning unit respectively. , The data is then integrated and a final decision is made based on a pre-built, scenario-based arbitration rule base, generating an explanatory compliance risk report and a vector of proposed modifications. Its decision expression is: in, Represents the set of candidate decisions. Let d be the degree of matching between decision d and the current scenario. This represents the weight of the k-th risk label. This represents the confidence level of the k-th risk label. , This is the balance coefficient.

[0010] Specifically, the graph neural network used by the contextual risk reasoning agent updates nodes through an aggregation function, the mathematical expression of which is: in, Represents a node In the The feature vector of the layer, For nodes The neighborhood group, For aggregate functions, Indicates the first The weight matrix of the layer, Represents a node In the The feature vector of the layer, It is the first The layer's bias vector, weight matrix, and bias vector are all learnable parameters. As an activation function, it captures complex compliance relationships in the graph through multi-layer propagation.

[0011] Preferably, the content generation agent adopts a conditional variational autoencoder structure, receives compliance constraint vectors and scene labels, and is used for multi-style and multilingual content generation and optimization.

[0012] Preferably, the content generation agent supports three generation modes: (1) Compliance-guided generation: Inject compliance constraint vectors during the generation process and guide content generation through the constraint decoding probability distribution; (2) Intelligent optimization rewriting: based on the aforementioned modification suggestion vector The rewriting process involves partial replacement or tone adjustment of the generated content, adhering to the principle of minimum edit distance and meeting compliance constraints. (3) Enhanced scene atmosphere: Based on scene tags, regionally friendly expressions or professional terms are retrieved from the industry corpus and integrated to enhance the cultural adaptability and scene appeal of the content.

[0013] Preferably, the interactive learning agent achieves multi-module collaborative updates through the following mechanism: (1) Knowledge base update mechanism: The feedback cases are structured and incrementally updated to the cultural compliance knowledge base in the form of triples; (2) Model incremental training mechanism: The labeled positive and negative samples are used for online fine-tuning of the semantic compliance analysis agent, the context risk reasoning agent and the content generation agent; (3) Arbitration strategy optimization mechanism: For records where the results of manual review are inconsistent with the engine's judgment, reinforcement learning is used to adjust the risk label weights of the dynamic arbitration center. and threshold.

[0014] Preferably, multi-module collaborative updates follow these rules: (a) When the cultural compliance knowledge base is updated, it triggers the re-embedding and lightweight retraining of the relevant models; (b) After the content generation agent is optimized, a compliance and consistency review is initiated to form a closed-loop iteration.

[0015] Preferably, the intent parsing model of the scene understanding agent adopts a multi-head attention mechanism, and its attention weight calculation expression is as follows: in, These are the query matrix, key matrix, and value matrix, respectively. The dimension of the key vector is used to extract and encode key intent features by multi-head attention weighting of the input content, and finally output the structured intent vector.

[0016] This multilingual content adaptive generation and cultural compliance verification system also includes its usage methods, comprising the following steps: S1: The scene-understanding agent receives and parses the user's original input, target market, scene tags, and language style parameters to generate a structured intent vector. ; S2: The cultural compliance intelligent agent, based on the cultural compliance knowledge base, performs semantic and contextual compliance checks on the parsed content through a compliance verification and arbitration engine, and outputs a compliance risk report and a vector of suggested modifications. ; S3: The content generation agent generates content based on the compliance risk report and modification suggestion vector. Based on scene tags and target market characteristics, multilingual content is generated or optimized after being retrieved from industry corpora; S4: The interactive learning agent collects feedback after deployment, and after structuring, drives the cultural compliance knowledge base, compliance verification and arbitration engine, and content generation agent to perform multi-module collaborative updates.

[0017] The four core intelligent agents here work together to build a closed-loop, adaptive, and evolvable content generation and compliance verification system. The agents are not simply connected in series, but rather achieve efficient collaboration through real-time data flow, knowledge sharing, and joint decision-making. The specific collaboration mechanism is as follows: Multi-agent collaborative process and data flow a) From Scene Understanding to Cultural Compliance: Structured Intent Vectors Output by Scene Understanding Agents Including information such as target market, scene tags, and sentiment, it provides accurate context review for cultural compliance intelligent agents, enabling them to perform contextualized semantic and linguistic analysis, avoiding mechanical matching out of context.

[0018] b) From cultural compliance to content generation: The compliance constraint vector and modification suggestion vector output by the cultural compliance agent are input into the content generation agent in real time, so that the compliance boundary is embedded in the early stage of generation, and the content generation is compliant, avoiding the multiple iterations of generation, review and modification in the traditional process.

[0019] c) From content generation to interactive learning: After the generated content is delivered, the interactive learning agent collects user feedback, compliance events and manual review records to form structured data, which drives the collaborative updates of the knowledge base, verification model and generation model.

[0020] d) From interactive learning to full system iteration: The interactive learning agent dynamically adjusts the arbitration strategy, optimizes the risk weight, and updates the knowledge graph through reinforcement learning and incremental training mechanisms, so as to achieve continuous self-optimization of the system during operation and form a closed loop of evolution while in use.

[0021] The system injects compliance constraints into the generation process in advance, ensuring that content conforms to target market standards during the creation stage. This significantly reduces the average number of iterations for content pairs and shortens the overall creation cycle.

[0022] Furthermore, a semantic compliance analysis agent performs literal review, while a contextual risk reasoning agent infers implicit risks. Through the collaboration of these two agents, the system can not only identify explicit violations but also infer potential risks by considering factors such as context and user groups. This significantly reduces the false negative and false positive rates in cross-cultural content review.

[0023] Meanwhile, the interactive learning agent drives the collaborative updating of the knowledge base, models, and strategies by collecting and disseminating feedback in real time, enabling the system to quickly respond to emerging cultural trends, regulatory changes, and platform policy updates. After the system was put into operation, the accuracy of compliance identification in new markets and new scenarios significantly improved.

[0024] Furthermore, while adhering to compliance constraints, the content-generating agent combines scene tags and target market characteristics to retrieve regional expressions from industry corpora, achieving a triple optimization of compliance, cultural adaptability, and marketing effectiveness. The generated content not only conforms to regulations but also possesses greater local relevance and persuasive power, improving content conversion rates in cross-border marketing.

[0025] Beneficial effects Through a multi-agent collaborative architecture, the system achieves deep integration of content generation and compliance verification, enabling autonomous decision-making and real-time interaction. Employing a dual semantic and contextual analysis mechanism, combined with knowledge graph and graph neural network technologies, it significantly reduces false positive and false negative rates. Furthermore, through closed-loop feedback and continuous learning mechanisms, the system can autonomously evolve based on market feedback, adapting to a dynamically changing compliance environment. It supports the generation of multi-style and multi-language content, incorporating regional cultural characteristics and expression habits to enhance the cultural adaptability and dissemination effectiveness of the content. Attached Figure Description

[0026] Various other advantages and benefits will become apparent to those skilled in the art upon reading the following detailed description of preferred embodiments. The accompanying drawings are for illustrative purposes only and are not intended to limit the invention. Throughout the drawings, the same reference numerals denote the same parts. In the drawings: Figure 1 This is a schematic diagram of the overall system architecture and multi-agent process for adaptive multilingual content generation and cultural compliance verification in cross-border e-commerce. Figure 2 This is an internal workflow diagram of a cultural compliance intelligent agent for a system that adaptively generates multilingual content and verifies cultural compliance in cross-border e-commerce. Figure 3 This is a diagram illustrating the collaborative working mechanism between the content generation intelligent agent and the cultural compliance intelligent agent in a system for adaptive multilingual content generation and cultural compliance verification for cross-border e-commerce. Figure 4 This is a schematic diagram of the data flow and update of a system interactive learning agent for multilingual content adaptive generation and cultural compliance verification in cross-border e-commerce. Figure 5 This is a schematic diagram of the cultural compliance knowledge base of a system for adaptive generation of multilingual content and verification of cultural compliance in cross-border e-commerce. Detailed Implementation

[0027] The following will refer to the appendix in the examples of this invention. Figure 1 -Appendix Figure 5 The technical solutions in the embodiments of the present invention are clearly and completely described. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of them. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are within the scope of protection of the present invention.

[0028] A system for adaptive generation and cultural compliance verification of multilingual content in cross-border e-commerce, characterized by comprising the following intelligent agents: 1. Scene Understanding Agent: This agent receives input parameters such as the user's original content, target market, scene tags, and multilingual styles. It then extracts relevant content through an intent parsing model and outputs a structured intent vector. ; 2. Cultural Compliance Intelligent Agent: This includes a cultural compliance knowledge base and a compliance verification and arbitration engine. The cultural compliance knowledge base stores structured cross-cultural compliance rules and multimodal cases in a hierarchical manner. 3. Content generation intelligent agent: It works in real time with the compliance verification decision module to receive the compliance constraint vector and modification suggestions output by the compliance verification decision module, and generate or optimize multilingual content that meets the requirements by combining market tags and target market characteristics; 4. Interactive Learning Agent: Used to transform user feedback, market compliance events, and manual review records after content delivery into structured feedback data, and drive multi-module collaborative updates of the cultural compliance knowledge base, the model in the compliance verification and arbitration engine, and the scene adaptive generation module.

[0029] The four core intelligent agents above cooperate to complete the entire closed loop process, from content input, compliance verification, multilingual generation to feedback evolution. The structure and function of each module will be explained in detail below.

[0030] (1) The scene-understanding agent, as the unified access point of the system, is responsible for receiving and processing multi-dimensional input information from the user. This information collectively constitutes the context and constraints for all subsequent processing flows. Specifically, the input information received by the agent includes: a. Raw Content: This mainly refers to the raw materials provided by the user that need to be processed or generated. These can take the form of text content such as product description drafts or advertising slogans; images requiring content description or compliance analysis; audio content that needs to be translated and analyzed; or video content for which keyframes and subtitles need to be extracted for multimodal analysis. At least one core piece of information must be input at a time.

[0031] b. Target Market Identifier: This identifier clearly indicates the country, region, or specific cultural and linguistic area where the content will ultimately be distributed. It serves as a key index for the system to access the corresponding cultural, legal, and platform compliance knowledge base.

[0032] c. Contextual Tags: These define the specific business or communication context in which the content is used. Typical contextual tags include, but are not limited to: product detail page descriptions, social media promotional ads, holiday-themed marketing, automated customer service replies, and brand press releases. These tags directly impact the rigor of compliance checks, the focus of contextual reasoning, and the style and format of the generated content.

[0033] Optional language style parameters: These refer to the stylistic tendencies of the generated content in the target language, which users can further specify. As a preferred approach, language style parameters can be categorized by formality, emotional tone, and audience affinity. Specifically, formality can be categorized from the rigorous formality of legal documents to the casual, conversational style of social media; emotional tone can be categorized, including but not limited to proactive promotion, neutral objectivity, and cautious disclosure; and audience affinity can include technical expressions geared towards professionals and popular explanations for general consumers.

[0034] The combination of these multi-dimensional information inputs enables the system to accurately understand the context, market, and processing of raw information into final content with a specific linguistic style. This provides precise contextual review for subsequent compliance verification decision-making modules and clear creative guidance for the scene-understanding agent.

[0035] More specifically, to further understand the user's input intent and transform this input into a numerical representation that a computer can process, the scene understanding agent internally incorporates an intent parsing model based on a multi-head attention mechanism. This model employs a specially trained natural language processing model with a Transformer encoder as its basic architecture. Through the multi-head attention mechanism, it extracts key intent features and encodes them into structured intent vectors. Its specific operation is as follows: The model first converts the text input into a sequence of word embeddings, then simultaneously computes multiple sets of attention weights through a multi-head attention layer. These attention weights are preferably in three sets: one set focuses on keywords related to core themes such as product name and core functions; another set focuses on capturing words and sentence structures expressing emotional inclinations; and a third set is used to associate common commercial intent patterns such as promotions, notifications, and comparisons. The outputs from these different attention heads are then concatenated and linearly transformed, and then fused and deepened through a feedforward neural network layer to form a holistic context-aware representation of the input content.

[0036] Finally, the model's output is connected to three specific classification or regression subtasks, corresponding to: a. Core theme classification: Determine the theme category to which the content belongs, including but not limited to electronics, clothing, and food; b. Sentiment Preference Score: Outputs a continuous value or discrete label representing sentiment polarity, which can include sentiment levels such as positive, neutral, and negative; c. Identifying Commercial Intent: Determining the dominant commercial intent behind the content, including but not limited to intents such as brand promotion, sales conversion, and user education.

[0037] After the content is output, it goes through a fully connected layer for concatenation and normalization, ultimately generating a fixed-dimensional structured intent vector. This structured intent vector It is a dense numerical vector that encodes unstructured, implicitly information-rich user input into a standardized mathematical representation containing multi-dimensional information such as theme, sentiment, and intent. It also serves as a crucial data bridge connecting the input to subsequent modules. It provides precise semantic context for the cultural compliance agent and clear creative goals for the content generation agent. In other words, the intent parsing model transforms the vague intent of user input into precise vectorized instructions, providing data support for subsequent compliance verification and adaptive generation. The specific model architecture used is based on existing technology; the publicly available mature architecture BERT and its multilingual variants are preferred. Therefore, the architecture itself will not be described in detail.

[0038] (2) The cultural compliance intelligent agent is the risk identification and decision-making center of this system, including the cultural compliance knowledge base and the compliance verification and arbitration engine. It is responsible for performing dual compliance verification of the content in terms of semantics and context, identifying explicit and implicit risks, and outputting modification suggestions.

[0039] The cultural compliance knowledge base transforms scattered, unstructured cross-cultural compliance knowledge into a structured form that machines can understand and reason about. It employs a three-tier architecture to systematically organize compliance knowledge, with each tier corresponding to different dimensions of constraint rules and cases. These three tiers specifically include: a. Cultural layer: Stores the customs and habits of the target market and region, the norms for the use of cultural elements, the cultural values ​​of the target market, cross-cultural historical norms, color and number preferences, compliance norms for festival culture, and the cultural connotations of common rhetoric and metaphors.

[0040] b. Legal layer: Stores explicit legal and regulatory provisions.

[0041] c. Platform Layer: Stores the specific operational policies, product listing guidelines, advertising review standards, platform-controlled product catalogs, and historical compliance warning cases of major cross-border e-commerce platforms, including but not limited to Amazon, Shopify, and AliExpress.

[0042] More specifically, the cultural compliance knowledge base uses knowledge graphs as its core data model to achieve precise knowledge association and complex reasoning. Among these: (a) Nodes: Represent entities or concepts in compliance knowledge, and the attributes of nodes support multimodal feature representation. Text nodes contain descriptive text and its semantic embedding vector; image nodes link to the original image and store its feature vector extracted through a visual model.

[0043] (b) Edges: Represent the relationships between nodes, defining rich semantic association types, mainly including: a. Conflict or violation: Indicates that a content element has a direct or potential violation relationship with a certain rule; b. Substitution or equivalence: Indicates that in a specific context, one element can be safely replaced by another element; c. Reinforcement or dependency: indicates that there is a mutual support or precondition relationship between certain rules or cases; d. Belongs to or applies to: Indicates that a case or element belongs to a specific legal or cultural category.

[0044] Building upon this foundation, the cultural compliance knowledge base employs a graph database as its storage engine, efficiently storing and systematically querying and exploring the aforementioned graph structure along relational paths. It also provides a graph-based query interface, supporting complex, multi-faceted queries. Furthermore, the cultural compliance knowledge base is dynamic; its updates are not only manually entered through the management backend but, more importantly, automatically performed through interactive learning agents. New compliance cases, manual review rulings, and new rules analyzed from market events are structured into new node-edge-node triples. After deduplication and confidence assessment, these triples are incrementally updated into the knowledge graph, enabling the knowledge base to continuously evolve.

[0045] Furthermore, the cultural compliance knowledge base will provide real-time knowledge services for the deep compliance verification and arbitration engine. These services specifically include: a. The semantic compliance analysis unit directly queries the graph to perform entity linking and explicit rule matching.

[0046] b. The contextual risk reasoning unit uses the parsed content and scenario as input for subgraph queries, performs multi-step reasoning on the graph, and discovers potential, indirect related risks.

[0047] Meanwhile, updates to the cultural compliance knowledge base will trigger the retraining or embedding updates of related analytical models, achieving the co-evolution of knowledge and models.

[0048] More specifically, the compliance verification and arbitration engine in the cultural compliance intelligent agent includes: (1) Semantic compliance analysis agent: used for literal review, adopts multilingual pre-trained model and knowledge graph embedding, identifies explicit violation elements in text or images based on the content of cultural compliance knowledge base, and outputs the first risk label set. Its mathematical expression is: in, As a risk label, Confidence level; (2) Contextual Risk Reasoning Agent: Used for additional review, it adopts graph neural network, combines relevant content such as scenario, product category and target user group, analyzes the potential hidden risks of the content, and outputs a second risk label set. Its mathematical expression is: (3) Dynamic Arbitration Center: Receives risk label sets generated by the semantic compliance agent and the early warning risk reasoning agent respectively. , The data is then integrated and a final decision is made based on a pre-built, scenario-based arbitration rule base, generating an explanatory compliance risk report and a vector of proposed modifications. Its decision expression is: in, Represents the set of candidate decisions. Let d be the degree of matching between decision d and the current scenario. This represents the weight of the k-th risk label. This represents the confidence level of the k-th risk label. , This is the balance coefficient.

[0049] Specifically, the graph neural network used by the contextual risk reasoning agent updates nodes through an aggregation function, the mathematical expression of which is: in, Represents a node In the The feature vector of the layer, For nodes The neighborhood group, For aggregate functions, Indicates the first The weight matrix of the layer, Represents a node In the The feature vector of the layer, It is the first The layer's bias vector, weight matrix, and bias vector are all learnable parameters. As an activation function, it captures complex compliance relationships in the graph through multi-layer propagation.

[0050] In particular, the content generation agent adopts a conditional variational autoencoder structure, which receives compliance constraint vectors and scene labels for multi-style and multilingual content generation and optimization.

[0051] More specifically, the content-generating agent supports three generation modes: (1) Compliance-guided generation: Inject compliance constraint vectors during the generation process and guide content generation through the constraint decoding probability distribution; (2) Intelligent optimization rewriting: based on the aforementioned modification suggestion vector The rewriting process involves partial replacement or tone adjustment of the generated content, adhering to the principle of minimum edit distance and meeting compliance constraints. (3) Enhanced scene atmosphere: Based on scene tags, regionally friendly expressions or professional terms are retrieved from the industry corpus and integrated to enhance the cultural adaptability and scene appeal of the content.

[0052] Specifically, the interactive learning agent achieves multi-module collaborative updates through the following mechanism: (1) Knowledge base update mechanism: The feedback cases are structured and incrementally updated to the cultural compliance knowledge base in the form of triples; (2) Model incremental training mechanism: The labeled positive and negative samples are used for online fine-tuning of the semantic compliance analysis agent, the context risk reasoning agent and the content generation agent; (3) Arbitration strategy optimization mechanism: For records where the results of manual review are inconsistent with the engine's judgment, reinforcement learning is used to adjust the risk label weights of the dynamic arbitration center. and threshold.

[0053] Furthermore, multi-module collaborative updates follow these rules: (a) When the cultural compliance knowledge base is updated, it triggers the re-embedding and lightweight retraining of the relevant models; (b) After the content generation agent is optimized, a compliance and consistency review is initiated to form a closed-loop iteration.

[0054] Specifically, the intent parsing model of the scene understanding agent adopts a multi-head attention mechanism, and its attention weight calculation expression is as follows: in, These are the query matrix, key matrix, and value matrix, respectively. The dimension of the key vector is used to extract and encode key intent features by multi-head attention weighting of the input content, and finally output the structured intent vector.

[0055] Building upon this, the multilingual content adaptive generation and cultural compliance verification system also outlines its usage methods, including the following steps: S1: The agent attempts to understand and parse the raw content of user input, target market, scene tags, and language style parameters to generate a structured intent vector. ; S2: The cultural compliance intelligent agent, based on the cultural compliance knowledge base, performs semantic and contextual compliance checks on the parsed content through a compliance verification and arbitration engine, and outputs a compliance risk report and a vector of suggested modifications. ; S3: The content generation agent generates content based on the compliance risk report and modification suggestion vector. Based on scene tags and target market characteristics, multilingual content is generated or optimized after being retrieved from industry corpora; S4: The interactive learning agent collects feedback after deployment, and after structuring, drives the cultural compliance knowledge base, compliance verification and arbitration engine, and scenario adaptive generation module to perform multi-module collaborative updates.

[0056] Example 1: In this embodiment, the system is applied to a scenario where a cross-border e-commerce company promotes a clothing product with specific cultural elements (such as geometric patterns) to a target market (taking a certain country as an example). The company needs to generate a product description in the local language that conforms to local cultural customs, while ensuring that the content complies with local cultural compliance requirements in terms of cultural customs and visual symbols.

[0057] The specific implementation process is as follows: S1. Scene Understanding: Intelligent Agent Performs Multi-Dimensional Intent Extraction and Environment Modeling The user inputs the following parameters through the contextualized input and parsing module: Original content (text): This jacket features a geometric embroidered pattern that symbolizes strength and good fortune.

[0058] b. Target market identifier: a certain country Scene tag: Product details page d language style parameters: formal, culturally respectful, and in line with local cultural values. The scene-understanding agent, based on the Transformer architecture's multi-head attention mechanism, performs deep semantic parsing of the input text to extract the following key intent dimensions: Theme identification: Clothing and embroidery products; Emotional inclination: Positive (auspicious, powerful); Commercial intent: Product attribute description and cultural value communication; Context: E-commerce product detail page, which needs to balance persuasiveness and compliance.

[0059] The scene-understanding agent encodes the aforementioned multi-dimensional information into a high-dimensional structured intent vector. Simultaneously, a lightweight environmental state representation is constructed, including target market cultural feature indexes, scenario constraints, etc., to provide executable context-aware inputs for downstream intelligent agents.

[0060] S2. Cultural compliance intelligent agent performs dynamic multimodal risk identification and interpretive decision-making. This intelligent agent consists of three core units: a semantic compliance analysis intelligent agent, a contextual risk reasoning intelligent agent, and a dynamic arbitration center, forming a three-layer decision-making chain of perception, reasoning, and adjudication. First, the semantic compliance analysis agent, based on a multilingual pre-trained model and cross-modal knowledge graph embedding, performs entity linking and symbol mapping for "geometric patterns" in the text. By querying the "country—geometric pattern—cultural symbol" association path in the cultural compliance knowledge base, it identifies that the symbol may have specific symbolic meaning in the local cultural context, with a confidence level of [insert confidence level here]. =0.88. The sub-agent outputs the first-layer risk label set. It includes a semiotic explanation and a description of potential conflicts.

[0061] Subsequently, the contextual risk reasoning agent employs a graph neural network to extract subgraphs and perform multi-hop reasoning on the knowledge graph. This agent constructs a dynamic context graph by combining factors such as product category (clothing), usage scenario (everyday outerwear), and audience profile (young people in a specific region). Through node information aggregation and relationship propagation, it identifies that "geometric patterns + clothing + public wearing" may trigger local cultural sensitivity, outputting a second-layer risk label. "Potential cultural sensitivity", confidence level =0.72, and generated a visual explanation of the risk propagation path.

[0062] Subsequently, the dynamic arbitration center received the first set of risk labels. With the second risk label set The system makes integrated decisions based on a scenario-based arbitration rule base. If the decision expression is determined to require modification after calculation, a modification opinion vector is generated. The system generates modification suggestions: it recommends replacing "geometric patterns" with "traditional art patterns" and explaining their artistic and design attributes to avoid cultural misunderstandings.

[0063] S3. Content generation agent performs multilingual stylized generation guided by compliance standards. The agent receives a structured intent vector from the scene understanding agent. Modification suggestion vectors for cultural compliance intelligent agents And scene tags, using a conditional variational autoencoder architecture for content generation: First, during the coding phase, the system injects compliance constraint vectors as condition variables into the latent space, which determines the direction of constraint generation. Then, in the decoding stage, the agent combines commonly used e-commerce expressions and affinity rhetoric templates from a certain country retrieved in real time from the industry corpus to perform style adaptation; Finally, generate localized language content that meets the requirements, ensuring that there are no artistic or design expressions that could cause cultural misunderstandings.

[0064] S4. Interactive learning agents implement closed-loop feedback and multi-model incremental evolution. After content is delivered, the system collects the following feedback signals through event tracking data and manual review interfaces: User interaction data: dwell time, sharing rate, purchase conversion; Compliance monitoring results: No complaints, no record of illegal removals; Human-annotated evaluation: Cultural compatibility score A.

[0065] Subsequently, the interactive learning agent transforms the above feedback into reinforcement learning reward signals, driving the following update process: Knowledge base enhancement: This case study is constructed as a triple and written into the cultural compliance knowledge base to enhance the weight of the "substitution relationship" edge.

[0066] Model fine-tuning: Lightweight incremental training of the GNN of the contextual reasoning sub-agent was performed using this batch of data to improve its sensitivity to the identification of risks related to "geometric patterns in clothing".

[0067] Strategy optimization: Based on the consistency data between manual review and system adjudication, the scenario weight parameters of the dynamic arbitration center are adjusted through a strategy gradient method to improve the accuracy of adjudication.

[0068] Generate Consistency Verification: Triggers the compliance consistency verification process of the content generation agent to ensure that its output distribution is aligned with the updated knowledge base.

[0069] Thus, the system completes a full closed loop of perception-generation-verification-learning, demonstrating the autonomous collaboration and continuous evolution capabilities of multiple agents in cross-cultural content generation tasks.

[0070] Example 2: In this embodiment, the system is applied to the generation and compliance review of local language promotional advertisements for a health food brand launching a "boost immunity" product series in a target market (taking a certain country as an example). The system must strictly comply with local food nutrition and health claim regulations while maintaining marketing effectiveness.

[0071] The specific implementation process is as follows: S1. Scene-understanding intelligent agents perform fusion analysis of marketing intent and legal context. User input parameters: Original content (text): This product can significantly enhance immunity and prevent colds.

[0072] b. Target market identifier: a certain country Scene tag: Social media advertising D language style parameters: aggressive promotion, trustworthy, compliant with local regulations At the same time, the agent automatically associates with the local legal knowledge subgraph and preloads relevant rule contexts such as the "Health Claims - Permitted Terminology" and "Prohibited Disease Treatment Statements".

[0073] S2. The cultural compliance intelligent agent initiates a dual-track risk assessment, primarily based on the legal layer. First, the semantic compliance analysis agent connects to the local legal knowledge base to identify that "preventing colds" is a prohibited disease prevention claim, violating relevant provisions of the local advertising law, and thus obtains a risk label. For "medical claims being illegal", confidence level =0.92. At the same time, this sub-agent provides original legal text citations and historical cases of violations for reference.

[0074] Subsequently, the contextual risk reasoning agent, combining relevant contextual nodes such as "health food," "social media," and "mass consumers," uses GNN reasoning to determine that the statement is likely to constitute misleading advertising and may trigger consumer complaints or regulatory review, thus outputting a risk label. Labeled as "potential legal risk", confidence level =0.78, and generate a risk impact assessment report. Following this, the dynamic arbitration center conducts arbitration based on a pre-set local advertising law rule base, determines that modifications are necessary, and generates a modification opinion vector. ,suggestion: (1) Change "preventing colds" to "supporting the health of the immune system"; (2) Add a compliance statement: "This statement has been reviewed and approved by the local food safety authority"; (3) Adjust the tone to “supportive statement” instead of “therapeutic claim”.

[0075] S3. Content-generating intelligent agents perform compliant rewriting and declaration embedding. The agent is based on modified opinion vectors. It adopts an "intelligent optimization and rewriting" mode to optimize the minimum edit distance without changing the marketing intent, including: (1) Delete the offending phrase "prevent colds"; (2) Insert the compliant statement "supports immune system health"; (3) Automatically add a note at the end of the local compliance statement; (4) Adjust the sentence structure to meet the fluency and persuasiveness standards of local language advertising slogans.

[0076] Ultimately, the output is a compliant expression that conforms to local language conventions, along with a compliance statement.

[0077] During the generation process, the content generation agent verifies in real time the consistency between the modified text and the "allowed health claims" list in the knowledge base, ensuring that the output meets both compliance and marketing requirements.

[0078] S4. Interactive learning agent optimizes strategies and iterates knowledge based on compliance audit results. 1. Update the compliance statement in the form of a triple to the legal knowledge graph; 2. Utilize manual review and confirmation results, and adjust the weight of "medical claims" risk labels in the dynamic arbitration center through a reinforcement learning mechanism. and decision threshold; 3. Initiate a compliance and consistency review to ensure that the subsequent output of the content generation agent is consistent with the updated knowledge base.

[0079] This embodiment demonstrates the system's compliance adaptive capabilities in highly regulated scenarios, its legal interpretive decision-making capabilities through multi-agent collaboration, and its feedback-based continuous strategy optimization mechanism, which significantly reduces the compliance risks and legal costs for enterprises in cross-border marketing.

Claims

1. A system for adaptive generation and cultural compliance verification of multilingual content in cross-border e-commerce, characterized in that, Includes the following modules: Scene-understanding agent: Receives input parameters such as raw user content, target market, scene tags, and multilingual styles. It extracts relevant content through an intent parsing model and outputs a structured intent vector. ; Cultural compliance intelligent agent: including a cultural compliance knowledge base and a compliance verification and arbitration engine. The cultural compliance knowledge base stores structured cross-cultural compliance rules and multimodal cases in a hierarchical manner. Content generation intelligent agent: collaborates in real time with the cultural compliance intelligent agent to receive compliance constraint vectors and modification suggestions output by the cultural compliance intelligent agent, and generates or optimizes multilingual content that meets the requirements by combining market tags and target market characteristics; Interactive learning agent: It is used to transform user feedback, market compliance events and manual review records after content delivery into structured feedback data, and drive the models in the cultural compliance knowledge base, compliance verification and arbitration engine and the content generation agent to perform multi-agent collaborative updates.

2. The system for adaptive generation of multilingual content and cultural compliance verification for cross-border e-commerce as described in claim 1, characterized in that... The cultural compliance knowledge base adopts a hierarchical graph structure for storage, specifically including rules and cases in the cultural layer, legal layer, and platform layer. The nodes of the graph structure support multimodal representation of text and image feature vectors, and its edge relationships include at least one association type among semantic conflict, substitution, and reinforcement.

3. The system for adaptive generation of multilingual content and cultural compliance verification for cross-border e-commerce as described in claim 1, characterized in that, The compliance verification and arbitration engine specifically includes: Semantic compliance analysis agent: Used for literal review, employing a multilingual pre-trained model and knowledge graph embedding, it identifies explicit violation elements in text or images based on the content of the cultural compliance knowledge base, and outputs a first risk label set. Its mathematical expression is: in, As a risk label, Confidence level; Contextual Risk Reasoning Agent: Used for additional review, it employs graph neural networks to analyze the potential implicit risks of content by combining relevant information such as scenario, product category, and target user group, and outputs a second set of risk labels. Its mathematical expression is: Dynamic Arbitration Center: Receives the risk label sets generated by the semantic compliance agent and the early warning risk reasoning agent, respectively. , The data is then integrated and a final decision is made based on a pre-built, scenario-based arbitration rule base, generating an explanatory compliance risk report and a vector of proposed modifications. Its decision expression is: in, Represents the set of candidate decisions. Let d be the degree of matching between decision d and the current scenario. This represents the weight of the k-th risk label. This represents the confidence level of the k-th risk label. , This is the balance coefficient.

4. The system for adaptive generation of multilingual content and cultural compliance verification for cross-border e-commerce as described in claim 3, characterized in that... The contextual risk reasoning agent uses a graph neural network to update nodes through an aggregation function, the mathematical expression of which is: in, Represents a node In the The feature vector of the layer, For nodes The neighborhood group, It is an aggregate function. Indicates the first The weight matrix of the layer, Represents a node In the The feature vector of the layer, It is the first The layer's bias vector, weight matrix, and bias vector are all learnable parameters. As an activation function, it captures complex compliance relationships in the graph through multi-layer propagation.

5. The system for adaptive generation of multilingual content and cultural compliance verification for cross-border e-commerce as described in claim 1, characterized in that... The content generation agent adopts a conditional variational autoencoder structure, receives compliance constraint vectors and scene labels, and is used for multi-style and multilingual content generation and optimization.

6. The system for adaptive generation of multilingual content and cultural compliance verification for cross-border e-commerce as described in claim 1, characterized in that, The content generation intelligent agent generation mode includes: Compliance-guided generation: Inject compliance constraint vectors into the generation process and guide content generation through the constraint decoding probability distribution; Intelligent optimization rewriting: based on the aforementioned modification suggestion vector The rewriting process involves partial replacement or tone adjustment of the generated content, adhering to the principle of minimum edit distance and meeting compliance constraints. Enhanced Contextual Atmosphere: Based on contextual tags, regionally relevant expressions or professional terms are retrieved from the industry corpus and incorporated to enhance the cultural adaptability and contextual appeal of the content.

7. The system for adaptive generation of multilingual content and cultural compliance verification for cross-border e-commerce as described in claim 1, characterized in that, The interactive learning agent achieves multi-module collaborative updates through the following mechanism: Knowledge base update mechanism: The feedback cases are structured and incrementally updated to the cultural compliance knowledge base in the form of triples; Model incremental training mechanism: The labeled positive and negative samples are used for online fine-tuning of the semantic compliance analysis agent, context risk reasoning agent and content generation agent; Arbitration strategy optimization mechanism: For records where the results of manual review are inconsistent with the engine's judgment, reinforcement learning is used to adjust the risk label weights of the dynamic arbitration center. and threshold.

8. The system for adaptive generation of multilingual content and cultural compliance verification for cross-border e-commerce as described in claim 7, characterized in that, The multi-agent collaborative update follows the following rules: When the cultural compliance knowledge base is updated, it triggers the re-embedding and lightweight retraining of the relevant models. Once the content is generated and the intelligent agent is optimized, a compliance and consistency review is initiated, forming a closed-loop iteration.

9. The system for adaptive generation of multilingual content and cultural compliance verification for cross-border e-commerce as described in claim 1, characterized in that, The intent parsing model of the scene understanding agent adopts a multi-head attention mechanism, and its attention weight calculation expression is as follows: in, These are the query matrix, key matrix, and value matrix, respectively. The dimension of the key vector is used to extract and encode key intent features by multi-head attention weighting of the input content, and finally output the structured intent vector.

10. A method of using a multilingual content adaptive generation and cultural compliance verification system for cross-border e-commerce, comprising the following steps: S1: The scene-understanding agent receives and parses the user's original input, target market, scene tags, and language style parameters to generate a structured intent vector. ; S2: The cultural compliance intelligent agent, based on the cultural compliance knowledge base, performs semantic and contextual compliance checks on the parsed content through the compliance verification and arbitration engine, and outputs a compliance risk report and modification suggestion vector. ; S3: The content generation agent generates content based on the compliance risk report and modification suggestion vector. Based on scene tags and target market characteristics, multilingual content is generated or optimized after being retrieved from industry corpora; S4: The interactive learning agent collects feedback after deployment, structures it, and drives the cultural compliance knowledge base, compliance verification and arbitration engine, and content generation agent to perform multi-agent collaborative updates.

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