An AI-friendly content generation method, system, medium, and program product

Abstract

The application belongs to the technical field of artificial intelligence auxiliary content generation and enterprise-level SaaS content operation, and discloses an AI-friendly content generation method, system, medium and program product. The method comprises the following steps: obtaining a content theme and a generation target, and forming an authorized knowledge package; establishing an arrangement rule to generate a plurality of candidate arrangement sequences, calculating an arrangement score and an AI-friendly quantitative score for each candidate sequence, and selecting a candidate arrangement sequence with the highest arrangement score and an AI-friendly quantitative score not lower than a preset score threshold as a target arrangement sequence; according to the target arrangement sequence, combining a semantic module set, a question and answer unit set and an evidence unit set to generate a structured content, and checking and correcting the structured content in combination with brand expression rules and content rules in an enterprise space to generate AI-friendly content. The application converts loose article generation into semantic modular generation, and improves content structure stability.

Description

Technical Field

[0001] This invention relates to the field of artificial intelligence-assisted content generation and enterprise-level SaaS content operation technology, and in particular to an AI-friendly content generation method, system, medium and program product. Background Technology

[0002] As generative artificial intelligence gradually becomes an important entry point for users to obtain brand, product, and service information, brand content is no longer just for human reading; it also needs to be more accurately understood, extracted, and reused by generative AI. Traditional articles are usually based on natural narration, with loose paragraph structure, and brand information, product features, applicable scenarios, user reviews, and conclusions / suggestions are mixed together, which can easily lead to difficulties in machine understanding.

[0003] Current AI content generation methods typically generate an article directly based on a topic or keywords. While this method improves creation efficiency, it still suffers from the following problems: 1. The generated content structure is unstable, lacking consistent semantic boundaries between different paragraphs; 2. Key elements such as brand name, product features, applicable scenarios, and evaluation criteria are not easily extracted; 3. The content lacks a direct question-and-answer structure, making it difficult to match users' expression patterns in AI question-and-answer scenarios; 4. Supporting materials, case studies, and evaluation summaries in the article do not form stable evidentiary units, resulting in insufficient credible expression; 5. Different departments or project teams within an enterprise have different requirements for content structure, brand expression, and risk rules, making it difficult to unify and manage these requirements using existing methods.

[0004] Therefore, how to provide an AI-friendly content generation method, system, medium, and program product is an urgent problem to be solved. Summary of the Invention

[0005] This invention provides an AI-friendly content generation method, system, medium, and program product to solve the problems mentioned above in the prior art.

[0006] According to a first aspect of the present invention, an AI-friendly content generation method is provided.

[0007] In one embodiment, the AI-friendly content generation method includes the following steps: The content theme and generation objective are obtained, and the generation objective is converted into an objective control vector. Based on the objective control vector, the authorized knowledge within the enterprise space is read to form an authorized knowledge package. Based on the content theme and authorized knowledge package, semantic elements are identified, and sets of semantic modules, question-and-answer units, and evidence units are generated respectively. When generating semantic modules, a dynamic boundary trimming mechanism is used to generate boundary values ​​for each semantic module based on the target control vector, semantic element type, evidence credibility, and risk level. Multi-dimensional trimming is performed based on the boundary values ​​when generating text sentence by sentence. Based on the user decision-making stage and semantic elements in the target control vector, an orchestration rule is established to generate multiple candidate orchestration sequences. An orchestration score and an AI-friendly metric score are calculated for each candidate sequence. The candidate orchestration sequence with the highest orchestration score and an AI-friendly metric score not lower than a preset score threshold is selected as the target orchestration sequence. Based on the target arrangement sequence, the semantic module set, question-and-answer unit set, and evidence unit set are combined to generate structured content. The structured content is then checked and corrected in conjunction with the brand expression rules and content rules in the enterprise space to generate AI-friendly content.

[0008] According to a second aspect of the present invention, an AI-friendly content generation system is provided.

[0009] In one embodiment, an AI-friendly content generation system includes: The authorized knowledge acquisition module is used to acquire content topics and generation objectives, convert the generation objectives into target control vectors, and read authorized knowledge within the enterprise space based on the target control vectors to form an authorized knowledge package; The content unit construction module is used to identify semantic elements based on the content theme and authorized knowledge package, and generate a set of semantic modules, a set of question-and-answer units, and a set of evidence units respectively. When generating semantic modules, a dynamic boundary pruning mechanism is used to generate boundary values ​​for each semantic module based on the target control vector, semantic element type, evidence credibility, and risk level. Multi-dimensional pruning is performed based on the boundary values ​​when generating text sentence by sentence. The content orchestration module is used to establish orchestration rules based on the user decision-making stage and semantic elements in the target control vector, generate multiple candidate orchestration sequences, calculate orchestration score and AI-friendly metric score for each candidate sequence, and select the candidate orchestration sequence with the highest orchestration score and AI-friendly metric score not lower than the preset score threshold as the target orchestration sequence. The content generation and verification module is used to combine the semantic module set, question and answer unit set, and evidence unit set to generate structured content according to the target arrangement sequence. It also verifies and corrects the structured content in combination with the brand expression rules and content rules in the enterprise space to generate AI-friendly content.

[0010] According to a third aspect of the present invention, a computer-readable storage medium is provided.

[0011] In one embodiment, a computer program is stored on the computer-readable storage medium, which, when executed by a processor, implements the steps of the above method.

[0012] According to a fourth aspect of the present invention, a computer program product is provided.

[0013] In one embodiment, the computer program product stores a computer program that, when executed by a processor, implements the steps of the method.

[0014] The technical solutions provided by the embodiments of the present invention may include the following beneficial effects: 1. This invention transforms loose article generation into semantic modular generation, improving the stability of content structure; enhances the adaptability of content to user question-and-answer expressions through question-and-answer units; improves the credibility of content expression through evidence units; makes content more suitable for generative AI understanding and extraction through arrangement rules; constrains content expression through brand knowledge base, reducing brand factual bias; supports multi-department and multi-brand collaboration through SaaS permission and rule management; and can be combined with content deployment plans to form a complete closed loop from topic planning to content generation.

[0015] 2. This invention uses a dynamic boundary trimming mechanism to subject each semantic module to multi-dimensional precise boundary constraints during text generation, such as field range, relationship depth, conclusion strength, evidence level, and brand disclosure. This differs from fixed template filling or uniform rule constraints, and improves the accuracy and controllability of the generated content.

[0016] 3. This invention reduces the risk of structural drift and improves generation efficiency by using a deviation correction matrix and a local correction mechanism to ensure that when content verification fails, the target unit and downstream nodes are only reverted in a targeted manner, rather than the entire text is regenerated.

[0017] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and are not intended to limit the invention. Attached Figure Description

[0018] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with the invention and, together with the description, serve to explain the principles of the invention.

[0019] Figure 1 This is one of the flowcharts illustrating an AI-friendly content generation method according to an exemplary embodiment; Figure 2 This is a block diagram illustrating the principle of an AI-friendly content generation system according to an exemplary embodiment; Figure 3 This is a second flowchart illustrating an AI-friendly content generation method according to an exemplary embodiment; Figure 4 This is a block diagram illustrating the components of an AI-friendly content generation system according to an exemplary embodiment. Detailed Implementation

[0020] The following description and accompanying drawings fully illustrate specific embodiments described herein to enable those skilled in the art to practice them. Some portions and features of certain embodiments may be included in or replace portions and features of other embodiments. The scope of the embodiments herein includes the entire scope of the claims and all available equivalents thereof. The various embodiments described herein are presented in a progressive manner, with each embodiment focusing on its differences from other embodiments; similar or identical parts between embodiments can be referred to interchangeably.

[0021] The modules in the apparatus or system of this application can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in a computer device in hardware form, or stored in the memory of a computer device in software form, so that the processor can call and execute the operations corresponding to each module.

[0022] Where there is no conflict, the embodiments and features in the embodiments of the present invention can be combined with each other.

[0023] Figure 1 An embodiment of an AI-friendly content generation method according to the present invention is shown.

[0024] In this optional embodiment, the AI-friendly content generation method includes the following steps: Step S101: Obtain the content theme and generation target, convert the generation target into a target control vector, and read the authorized knowledge in the enterprise space according to the target control vector to form an authorized knowledge package; Step S102: Based on the content theme and authorized knowledge package, identify semantic elements and generate a set of semantic modules, a set of question-and-answer units, and a set of evidence units respectively; when generating semantic modules, combine the dynamic boundary trimming mechanism to generate boundary values ​​for each semantic module according to the target control vector, semantic element type, evidence credibility, and risk level, and perform multi-dimensional trimming based on the boundary values ​​when generating text sentence by sentence; Step S103: Based on the user decision-making stage and semantic elements in the target control vector, establish orchestration rules to generate multiple candidate orchestration sequences, calculate orchestration score and AI-friendly metric score for each candidate sequence, and select the candidate orchestration sequence with the highest orchestration score and AI-friendly metric score not lower than the preset score threshold as the target orchestration sequence. Step S104: Based on the target arrangement sequence, combine the semantic module set, question and answer unit set, and evidence unit set to generate structured content, and combine the brand expression rules and content rules in the enterprise space to check and correct the structured content to generate AI-friendly content.

[0025] In this optional embodiment, the steps of obtaining the content topic and generation target, converting the generation target into a target control vector, and reading authorized knowledge within the enterprise space based on the target control vector to form an authorized knowledge package include the following steps: receiving the content topic and generation target from the content deployment plan, and converting the generation target into a target control vector through preset conversion rules; determining the authorization scope based on the permission indication information in the target control vector and the current user's role and permissions in the enterprise space, and retrieving candidate knowledge according to the content topic within the authorization scope; reviewing and verifying the candidate knowledge, and removing candidate knowledge that fails the review and verification to form an authorized knowledge package.

[0026] In this optional embodiment, the step of identifying semantic elements based on the content topic and authorized knowledge package, and generating a semantic module set, a question-and-answer unit set, and an evidence unit set respectively, includes the following steps: performing word segmentation, intent recognition, and scene classification on the content topic, and extracting knowledge fields from the authorized knowledge package, identifying semantic elements after synonym normalization and entity alignment; identifying the material types in the authorized knowledge package, and deduplicating the materials in the authorized knowledge package to obtain the evidence unit set; generating a semantic module set based on the semantic elements, and through module candidate determination, element binding, boundary verification, and text generation operations, combined with a dynamic boundary trimming mechanism; establishing a corresponding question intent pool based on the user decision-making stage in the target control vector, extracting key elements from the semantic module set to convert them into candidate question templates, generating question text, and generating answer text according to a four-segment structure to obtain the question-and-answer unit set.

[0027] In this optional embodiment, the steps of identifying the material types in the authorized knowledge package and deduplicating the materials in the authorized knowledge package to obtain the evidence unit set include the following steps: identifying the material types in the authorized knowledge package that include case materials, evaluation materials, and proof materials; performing deduplication processing including structural deduplication, semantic deduplication, and evidence deduplication on each material according to the material type, and performing source verification on the deduplicated materials; performing sensitive information filtering on the materials that pass the deduplication and source verification, generating corresponding evidence units according to the material type, and writing a source number and credibility identifier to each evidence unit to form an evidence unit set.

[0028] In this optional embodiment, the steps of performing word segmentation, intent recognition, and scenario classification on the content topic, and extracting knowledge fields from the authorized knowledge package, and identifying semantic elements after synonym normalization and entity alignment, include the following steps: performing word segmentation on the content topic to obtain core topic words, limited topic words, and risk topic words, and identifying the user's current idea graph and candidate intent; matching the core topic words, limited topic words, and risk topic words with a preset scenario tag tree in the enterprise space, and classifying the content topic into scenarios through semantic similarity, field hit rate, and evidence coverage calculations to obtain scenario classification results; and classifying the content topic into scenarios based on the scenario classification results. For each category result, evaluation materials and case materials related to the scenario classification results are retrieved from the authorized knowledge package. Evidence back-annotation is then performed on these materials to generate limiting elements, applicable boundary elements, and elements to be verified. Based on the idea graph and candidate intents, corresponding entity attribute fields are extracted from the authorized knowledge package. These fields are then normalized using a pre-defined thesaurus, and the normalized fields are aligned with the standard entity library in the enterprise space to generate standardized semantic features. These standardized semantic features are then integrated with the limiting elements, applicable boundary elements, and elements to be verified to obtain semantic elements.

[0029] In this optional embodiment, the step of generating a semantic module set based on semantic elements, through module candidate determination, element binding, boundary verification, and text generation operations, combined with a dynamic boundary trimming mechanism, includes the following steps: Based on the content theme and the user decision-making stage in the target control vector, determine the semantic module candidate type from a preset module type library, and bind the identified semantic elements to the corresponding semantic module candidate type to generate an initial semantic module object; perform boundary verification on the initial semantic module object, and use the initial semantic module object that passes the boundary verification as the final semantic module object; calculate the dynamic boundary value of each final semantic module object by reading the brand visibility level, verification rigor, and user decision-making stage in the target control vector, combined with the type of the semantic module object, the bound semantic element set, and the credibility of the evidence; generate a sentence skeleton based on the final semantic module object, and fill the sentence skeleton with content according to the dynamic boundary value to generate initial module text; reverse-parse the initial module text into fact items, conclusion items, and restriction items, and verify them with a temporary semantic relationship graph and evidence units, and use the initial module text that passes the verification as the final semantic module set.

[0030] In this optional embodiment, the boundary verification includes: field boundary verification, relation boundary verification, stage boundary verification, and evidence boundary verification; the field boundary verification is used to determine whether the knowledge field referenced by the initial semantic module belongs to the set of allowed fields of the initial semantic module; the relation boundary verification is used to determine whether there is a valid path between the brand, product, scenario, evaluation, and evidence in the temporary semantic relationship graph within the initial semantic module; the stage boundary verification is used to determine whether the expression of the initial semantic module conforms to the user decision-making stage in the target control vector; and the evidence boundary verification is used to determine whether the deterministic conclusion in the initial semantic module is covered by the corresponding evidence unit.

[0031] In this optional embodiment, the step of calculating the dynamic boundary value of each final semantic module object by reading the brand visibility level, verification rigor, and user decision stage from the target control vector, combined with the type of the semantic module object, the set of bound semantic elements, and the credibility of the evidence, includes the following steps: reading the module type and the set of bound semantic elements of the current final semantic module object, and extracting the brand visibility level, verification rigor, and user decision stage from the target control vector; by querying the allowed field template corresponding to the current module type, and taking the intersection of the field set of the field template and the knowledge fields corresponding to the bound semantic elements, removing the prohibited fields and fields outside the role permissions hit by the target control vector, the field range boundary is obtained; by statistically analyzing the current... The average path length of the core entities of the previous final semantic module object in the temporary semantic relationship graph is used, combined with the module type and the strictness of verification, to determine the maximum number of hops for semantic relationship inference; the average evidence score of the evidence units bound to the current final semantic module object is evaluated, and the upper limit level of conclusion expression strength is obtained by combining the two-level threshold judgment; the minimum evidence credibility level required is determined in reverse based on the upper limit level of conclusion expression strength; the brand visibility boundary is obtained by mapping the brand visibility level in the target control vector to the upper limit of the frequency of occurrence of the corresponding brand name; and the dynamic boundary value of the final semantic module object is obtained by integrating the field range boundary, the maximum number of hops for semantic relationship inference, the upper limit level of conclusion expression strength, the minimum evidence credibility level, and the brand visibility boundary.

[0032] In this optional embodiment, the steps of establishing a corresponding question intent pool based on the user decision-making stage in the target control vector, extracting key elements from the semantic module set to convert them into candidate question templates, generating question text, and generating answer text according to a four-segment structure to obtain a question-answer unit set include the following steps: determining the priority of question intents based on the user decision-making stage in the target control vector and establishing a corresponding question intent pool; reading the module purpose, key elements, and gap elements of each semantic module from the generated semantic module set, combining them with the question intent pool to generate candidate question templates, and obtaining a question text set through filtering; for each question in the question text set, generating answer text according to a four-segment structure, and performing slot closure verification on the answer to obtain a question-answer unit set.

[0033] In this optional embodiment, the step of establishing orchestration rules based on the user decision-making stage and semantic elements in the target control vector to generate multiple candidate orchestration sequences, calculating an orchestration score and an AI-friendly metric score for each candidate sequence, and selecting the candidate orchestration sequence with the highest orchestration score and an AI-friendly metric score not lower than a preset score threshold as the target orchestration sequence includes the following steps: mapping content targets to structural targets, mapping user decision-making stages to information priorities, and generating order constraints, question-and-answer insertion position constraints, evidence insertion position constraints, and brand explicitness / implicitness constraints based on structural targets and information priorities to form orchestration rules; generating multiple candidate orchestration sequences based on the orchestration rules, and calculating an orchestration score and an AI-friendly metric score for each candidate orchestration sequence; sorting all candidate sequences from high to low according to their orchestration scores, and selecting the candidate orchestration sequence with the highest orchestration score and an AI-friendly metric score not lower than a preset score threshold, which is then determined as the final target orchestration sequence.

[0034] In this optional embodiment, the step of combining the semantic module set, question-and-answer unit set, and evidence unit set to generate structured content according to the target arrangement sequence, and verifying and correcting the structured content in conjunction with the brand expression rules and content rules in the enterprise space to generate AI-friendly content includes the following steps: constructing a content skeleton containing paragraph levels, insertion order, question-and-answer slots, evidence slots, and conclusion slots; embedding semantic modules, question-and-answer units, and evidence units into the corresponding slots step by step according to the target arrangement sequence, and inserting structured separator markers to generate initial structured content; performing multiple verification processes on the initial structured content in sequence, and generating a status identifier for each verification result; correcting the initial structured content that fails the verification according to the status identifier, and using the corrected structured content as AI-friendly content.

[0035] In this optional embodiment, the multiple checks include: structural check, fact check, evidence check, question-and-answer consistency check, brand rule check, and risk check.

[0036] Figure 2 An embodiment of an AI-friendly content generation system according to the present invention is shown.

[0037] In this optional embodiment, an AI-friendly content generation system includes: The authorized knowledge acquisition module 201 is used to acquire the content topic and generation target, convert the generation target into a target control vector, and read the authorized knowledge in the enterprise space based on the target control vector to form an authorized knowledge package; The content unit construction module 202 is used to identify semantic elements based on the content theme and authorized knowledge package, and generate a set of semantic modules, a set of question-and-answer units, and a set of evidence units respectively. When generating semantic modules, a dynamic boundary pruning mechanism is used to generate boundary values ​​for each semantic module based on the target control vector, semantic element type, evidence credibility, and risk level. Multi-dimensional pruning is performed based on the boundary values ​​when generating text sentence by sentence. The content orchestration module 203 is used to establish orchestration rules based on the user decision-making stage and semantic elements in the target control vector, generate multiple candidate orchestration sequences, calculate orchestration score and AI-friendly metric score for each candidate sequence, and select the candidate orchestration sequence with the highest orchestration score and AI-friendly metric score not lower than the preset score threshold as the target orchestration sequence. The content generation and verification module 204 is used to combine the semantic module set, question and answer unit set and evidence unit set to generate structured content according to the target arrangement sequence, and to verify and correct the structured content in combination with the brand expression rules and content rules in the enterprise space to generate AI-friendly content.

[0038] To facilitate understanding of the above technical solutions of the present invention, the following further explains the above technical solutions of the present invention from the perspective of architecture and principle, such as... Figures 3-4 As shown, the details are as follows: The purpose of this invention is to provide an AI-friendly content generation method based on the arrangement of semantic modules, question-and-answer units, and evidence units, enabling enterprises to transform brand knowledge, product knowledge, and content themes into content that is clearly structured, well-supported by evidence, adaptable to questions and answers, and consistent with the brand.

[0039] To reduce the risk of overlap with content generation schemes after ordinary semantic parsing, this invention further limits the content generation process to a processing chain of permission-based knowledge reading, multi-source semantic element alignment, generation of three types of objects: semantic modules, question-answering units, and evidence units, object arrangement based on the decision-making stage, and evidence closed-loop verification. Each type of object retains its source field, applicable stage, expression boundary, and verification status, so that the generated content is not simply obtained by directly expanding the topic, but is formed by rule arrangement of traceable structured objects.

[0040] Specifically, the following steps are included: Step 1: Receive Content Topic and Generation Goal; The system receives the content topic and generation goal. The content topic can come from the content deployment plan, such as recommendations for commuting-time energy coffee, how to choose enterprise content operation tools, and which teams are suitable for a particular product. The generation goal can include brand description, product recommendation, scenario introduction, Q&A explanation, word-of-mouth description, or purchase advice. The system also receives parameters such as content type, target users, user decision-making stage, brand visibility level, and content length.

[0041] The generated objective is not simply saved as an input parameter in subsequent steps, but is transformed into a target control vector G={goalType,contentType,audience,stage,brandExposure,lengthLimit,riskLevel}. Here, goalType defines the main output purpose of the content, contentType determines the output template, audience is used to filter target audience and contextual elements, stage determines the decision-making stage, brandExposure determines the visibility of brand information, lengthLimit constrains the module length, and riskLevel determines the rigor of the verification process.

[0042] Specifically, in step two, the system adjusts the authorized knowledge retrieval scope based on the target control vector G; in step three, the system assigns target weights to semantic elements based on G; in steps four and five, the system selects the semantic module type and question-answering intent type based on G; in steps seven and eight, the system determines the module order, evidence insertion position, and text expression method based on G; and in step nine, the system determines the verification thresholds for prohibited expressions, evidence strength, and risk warnings based on G. Thus, the generated target participates in the entire process control from knowledge retrieval to final output, and is used for subsequent content generation, structural arrangement, and rule verification.

[0043] Step Two: Retrieve Authorized Knowledge from the Enterprise Space; Based on role and knowledge permissions within the Enterprise Space, the system retrieves authorized brand knowledge, product knowledge, case studies, evaluation materials, and content rules. This step only utilizes knowledge and rules already maintained within the Enterprise Space.

[0044] Specifically, brand knowledge, product knowledge, case materials, evaluation materials, and content rules are pre-maintained in the enterprise space's knowledge base or rule base, or can be stored through manual entry or written after review. Each knowledge record must include at least the knowledge type, applicable brand or product, source identifier, version number, effective date, authorizing department, visible role, fields that can be used for generation, and restricted fields that cannot be used for generation.

[0045] During the reading process, the system first determines the authorization scope based on the current enterprise space, generated task, operator role, and content objectives. Then, within the authorized scope, it retrieves candidate knowledge according to content theme keywords, synonyms, product tags, scenario tags, and decision-making stage tags. Subsequently, expired, unapproved, mismatched with the current brand visibility level, or unavailable sources are removed, forming the authorized knowledge package for this task. The authorized knowledge package retains the source number and field-level permissions for each record for subsequent evidence binding and content verification.

[0046] The difference between the above knowledge retrieval method and the conventional external search method is that this step only uses the knowledge and rules pre-maintained within the enterprise space, and does not retrieve data from outside the enterprise space, ensuring the controllability and traceability of the generated content. Each knowledge record contains field-level permission identifiers (fields that can be used for generation and restricted fields that cannot be used for generation). When reading, the system performs four-fold verification of space authorization, role authorization, field authorization, and time-limited authorization. Knowledge fields that fail any authorization check cannot enter the subsequent generation process.

[0047] Step 3: Identify semantic elements; the system identifies semantic elements from content themes, brand knowledge, and product knowledge. Semantic elements can include brand elements, product elements, scenario elements, target audience elements, evaluation elements, evidence elements, and conclusion elements.

[0048] The specific identification process includes: first, segmenting the content topic, identifying intent, and classifying scenarios to obtain topic words, user tasks, usage scenarios, and initial decision-making stages; second, extracting brand positioning, prohibited brand expressions, product functions, parameter ranges, target audience, price or budget range, usage restrictions, evaluation opinions, and supporting material tags from the authorized knowledge package; and third, performing synonym unification and entity alignment with the above fields to obtain the association relationships between brand, product, scenario, evaluation, and evidence.

[0049] When segmenting content topics, the system first performs character cleaning, brand name protection, and product name protection on the input topic, treating registered brand names, product names, function names, model names, and industry terms in the enterprise knowledge base as indivisible words. Then, it uses a general word segmentation dictionary and an enterprise-specific dictionary for joint word segmentation to obtain a set of candidate topic words. Subsequently, based on part of speech, the number of hits in the authorized knowledge package, the degree of matching with the generation target, and whether they are prohibited or risky words, the candidate topic words are filtered to obtain core topic words, limited topic words, and risky topic words.

[0050] During intent recognition, the system inputs core keywords, generation goals, content types, and user decision-making stages into the intent classifier or rule mapping table to identify intents such as explanations, recommendations, comparisons, proofs, question-and-answer explanations, risk warnings, or purchase suggestions. When multiple intent scores output by the classifier are close, the system determines the intent graph in the order of priority: generation goals, followed by user decision-making stages, and then content types, and retains the secondary intents as candidate intents for subsequent question-and-answer units.

[0051] When classifying scenarios, the system matches keywords with a pre-defined scenario tag tree in the enterprise space. The scenario tag tree includes at least six types of nodes: usage location, usage task, user group, usage time, usage restrictions, and alternative solutions. The system calculates the semantic similarity, field hit rate, and evidence coverage between keywords and each scenario node, and selects the scenario with the highest score and authorized knowledge support as the main scenario. The remaining scenarios that meet the threshold are designated as supplementary scenarios or risk scenarios.

[0052] To differentiate it from conventional keyword extraction, the system also performs reverse evidence annotation on evaluation and case materials: when specific effects, applicable conditions, limitations, or risk warnings appear in the evaluation or case, the system not only extracts positive selling points, but also simultaneously generates limiting elements, applicable boundary elements, and elements to be verified; and compares these elements with the corresponding brand knowledge and product knowledge for consistency. Only elements that simultaneously possess topic relevance, knowledge authorization status, and evidence support relationship are generated in subsequent modules.

[0053] The differences between the above semantic element recognition method and conventional keyword extraction are as follows: (1) The system performs reverse evidence annotation on the evaluation materials and case materials, not only extracting positive selling points, but also generating restrictive elements, applicable boundary elements and elements to be verified at the same time, forming a two-way annotation mechanism; (2) The determination of topic relevance includes the joint determination of three conditions: text relevance, relationship relevance and target relevance. Only when all three conditions are met or two of them are met and there is highly credible evidence to support them is the topic relevance determined; (3) The system constructs a temporary semantic relationship graph with standard entities as nodes and relationships such as brand affiliation and product inclusion as edges, and verifies the alignment effectiveness through one-hop or two-hop paths, which is different from simple keyword matching or vector similarity calculation.

[0054] Specifically, the synonym unification and entity alignment include: the system first generates standardized field names for keywords, brand fields, product fields, evaluation fields, and case fields, removing spaces, capitalization differences, alias suffixes, and industry slang; then it queries the alias table, synonym table, brand product mapping table, and knowledge graph nodes in the enterprise space to uniformly map the abbreviation and full name of the same brand, the marketing expression and technical expression of the same function, and the colloquial expression and standard label of the same scenario to the same standard entity.

[0055] After unification, the system constructs a temporary semantic relationship graph for this task, using standard entities as nodes and brand affiliation, product inclusion, functional applicability, scenario adaptation, evaluation orientation, evidence support, and constraints as edges. When a valid one- or two-hop path exists between a keyword and a field, and no disabled or conflicting relationships exist within that path, the entities are considered successfully aligned. If multiple entities can be aligned, the system prioritizes the entity that matches the generation target, user decision-making stage, and evidence unit simultaneously, recording the other entities as candidate ambiguities for review during the verification process.

[0056] The determination of topic relevance includes three aspects: text relevance, relation relevance, and target relevance. Text relevance is determined by the similarity or hit rate between the topic words and the knowledge field. Relationship relevance is determined by the path distance and relation type in the temporary semantic relationship graph. Target relevance is determined by the degree of matching between the knowledge field and the target control vector G. The topic is considered to be relevant when all three aspects meet the preset threshold, or when two of them meet the threshold and are supported by highly credible evidence units.

[0057] The determination of knowledge authorization status includes spatial authorization, role authorization, field authorization, and time-limited authorization. The system checks whether the enterprise space to which the knowledge record belongs is consistent with the current task, whether the operator role has read permissions, whether the called field is allowed to be used for content generation, and whether the knowledge version is in effect. If any authorization check fails, the knowledge field cannot be included in the semantic element set. The determination of evidence support relationship includes three aspects: traceability of source, matching of evidence type, and coverage of conclusion. Only when the source number of the evidence unit is traceable, the evidence type is consistent with the target conclusion type, and the evidence summary can cover the semantic element to be supported, is an evidence support relationship determined.

[0058] Step 4: Generate a set of evidence units; the system generates a set of evidence units based on the case materials, evaluation materials, and supporting materials maintained within the enterprise space. An evidence unit includes a case summary unit, an evaluation summary unit, a proof explanation unit, and a note explanation unit. Each evidence unit contains the evidence type, corresponding semantic module, summary text, applicable content stage, and credibility indicator.

[0059] It should be noted that an evidence unit can be represented as E={type, summary, target, reliability}. Here, type represents the type of evidence, summary represents the abstract text, target represents the corresponding semantic module or conclusion, and reliability represents the credibility indicator.

[0060] When generating evidence units, the system first identifies the material type. Case materials generate a case summary unit, which includes the user, usage scenario, problem solved, result description, and applicable boundaries. Evaluation materials generate an evaluation summary unit, which includes the evaluation object, evaluation viewpoint, evaluation tendency, frequency or representativeness indicators, and whether it contains restrictive opinions. Proof materials generate a proof explanation unit, which includes the proof object, proof source, proof fields, applicable period, and the type of conclusion that can be supported.

[0061] Before generating a summary or evidence, the system performs deduplication, source verification, and sensitive information filtering on the materials. For materials that do not directly support the conclusion but can indicate conditions or risks, a note unit is generated. Each evidence unit includes a source number, an index of the original material fragment, supported semantic elements, unsupported exaggerated statements, and a credibility indicator. Subsequent content generation can only cite this evidence within the scope allowed by the evidence unit.

[0062] Material deduplication includes structural deduplication, semantic deduplication, and evidence deduplication. Structural deduplication first generates a structural fingerprint based on the title, body text, table style, image description, evaluation field format, and source number of the material. This fingerprint is used to identify duplicate uploads, format conversions, or field copies of the same material. Semantic deduplication then generates a semantic fingerprint based on the core facts, evaluation viewpoints, scene descriptions, and result descriptions in the material. This fingerprint is used to identify materials with different wording but the same meaning. Evidence deduplication determines whether multiple materials point to the same case, the same evaluation subject, or the same source of proof, preventing the same fact from being counted repeatedly in the strength of evidence.

[0063] Source verification includes source entity verification, source link verification, and version verification. The system checks whether the materials have the uploader, reviewer, source channel, writing time, authorized scope, and version number; for materials written in batches, the system records the writing batch, writing time, and original number; for manually entered materials, the system requires recording the person who entered the material and the review status. Materials with unclear source entities, unreviewed materials, materials exceeding the authorized scope, or expired versions cannot generate proof and explanation units and can only be used as materials pending confirmation or filtered out.

[0064] Sensitive information filtering includes sensitive entity identification, sensitive relationship identification, and alternative expression generation. Based on the enterprise's configured sensitive word list, personal information rules, customer name protection rules, competitor comparison rules, and compliance expression rules, the system identifies information such as names, mobile phone numbers, email addresses, precise addresses, undisclosed customer names, unauthorized prices, contract amounts, internal indicators, negative competitor statements, and absolute performance promises. Simultaneously, it combines material type and text style to determine the location of sensitive information, such as customer names in table cells, account names in review screenshots, and project names in case study titles.

[0065] After filtering, the system does not simply delete all sensitive information. Instead, it generates alternative expressions based on the type of sensitivity: personal identification information is replaced with role or group tags, customer names are replaced with industry or scale tags, precise numerical values ​​are replaced with authorized ranges or qualitative descriptions, unauthorized project names are replaced with scenario descriptions, and negative expressions about competitors are replaced with neutral comparative dimensions. The replaced content still retains the original material fragment index and desensitization mapping identifiers, allowing enterprise administrators to trace it within the authorized scope.

[0066] Step 5: Generate a set of semantic modules; the system generates a set of semantic modules based on the identified semantic elements. A semantic module is a content unit with a clear theme and boundaries, and may include an overview module, brand module, product module, scenario module, comparison module, evaluation module, risk warning module, and conclusion / suggestion module. Each semantic module includes a module title, module purpose, available knowledge fields, expression rules, and output text.

[0067] It should be noted that a semantic module can be represented as M={m1,m2,...,mn}. Each mi contains the module type, module goal, available knowledge fields, expression rules, and output text.

[0068] The generation of semantic modules includes four steps: module candidate determination, feature binding, boundary verification, and text generation. After the semantic modules are generated, the evidence candidate units are bound to the corresponding semantic modules according to target_claim, module_id, and slot_id.

[0069] Specifically, the system first selects candidate modules from a pre-set module type library based on content objectives and user decision-making stages; then it binds the identified semantic elements to different modules according to module type. For example, it binds brand positioning and brand differentiation points to the brand module, functions, parameters and usage restrictions to the product module, and evaluation opinions and case tags to the evaluation module or evidence receiving module.

[0070] Subsequently, the system generates a module object for each module. The module object includes at least the module number, module type, module title, module purpose, set of input elements, allowed knowledge fields, prohibited expression fields, associated question-and-answer types, associated evidence types, output length, and verification rules. If the same semantic element hits multiple modules simultaneously, the system determines the main module according to the rules of evidence priority, risk independence, and conclusion deferred, and only retains the citation relationship in other modules to avoid repeated expansion of the same evidence or conflicting conclusions between different modules.

[0071] Boundary checks specifically include field boundary checks, relationship boundary checks, stage boundary checks, and evidence boundary checks. Field boundary checks determine whether the knowledge fields referenced by a module belong to the module's allowed field set; relationship boundary checks determine whether there are valid paths between brands, products, scenarios, evaluations, and evidence within a module in the temporary semantic relationship graph; stage boundary checks determine whether the module's expression conforms to the current user decision-making stage, for example, strong conversion promises should not be prematurely output in the cognition stage, and restrictive conditions should not be omitted in the comparison stage; evidence boundary checks determine whether the deterministic conclusions in a module are covered by the corresponding evidence units.

[0072] To avoid creative overlap with conventional modular generation schemes, the system introduces a dynamic boundary pruning mechanism. Based on the target control vector G, semantic element type, evidence credibility, and risk level, the system generates boundary values ​​for each module. B′={fieldScope,relationDepth,claimStrength,evidenceLevel,exposureLevel}; When the module text generation process contains knowledge fields exceeding fieldScope, weak relation inferences exceeding relationDepth, conclusion strength exceeding claimStrength, evidence citations below evidenceLevel, or brand exposure exceeding exposureLevel, the system will automatically prune the corresponding content or downgrade it to a conservative statement.

[0073] The Chinese meanings of the variables in boundary value B′ are as follows: fieldScope represents the range of knowledge fields that this module is allowed to reference, that is, the boundary of the set of fields that this module can reference from the authorized knowledge package when generating text; relationDepth represents the maximum number of hops for semantic relation inference, that is, the length of the furthest relation path that can be inferred in the temporary semantic relation graph starting from the core entity of the current module; claimStrength represents the upper limit level of the strength of the conclusion expression, that is, the highest level of expression that the deterministic conclusion in the module is allowed to use, for example, the most suitable is high strength, the more suitable is medium strength, and can be used as a reference is low strength; evidenceLevel represents the minimum level of confidence required to support the evidence, that is, the deterministic conclusion in the module must be covered by evidence units of no less than this level; exposureLevel represents the maximum degree of exposure of brand information, that is, the upper limit of the frequency and prominence of brand name, brand logo, and brand association statements in the module text.

[0074] The steps for generating the boundary value B′ are as follows: Step (1): The system reads the module_type of the current module and the set of bound semantic elements, and extracts brandExposure, riskLevel, and stage from the target control vector G.

[0075] Step (2): The system calculates fieldScope. Specifically, the system queries the allowed field template corresponding to the module type, takes the intersection of the template field set with the knowledge fields corresponding to the semantic elements already bound to the current module, and then removes the fields matched by forbidden_terms in the target control vector G and the fields not allowed by the role permission_scope to obtain fieldScope. Here, the target control vector G means the generation target control vector, which is used to record the target constraints of this content generation task; forbidden_terms represents the set of prohibited words or fields, and role_scope represents the range of knowledge fields authorized to be called by the current operator role. fieldScope means the range of available fields, that is, the set of knowledge fields that are allowed to be written into the current semantic module after being constrained by module type, semantic elements, prohibited fields and role permissions.

[0076] Step (3): The system calculates relationDepth. Specifically, the system calculates the average path length of the core entities of the current module in the temporary semantic relationship graph. If the module type is an overview module or a scenario module, relationDepth is set to 2 by default (allowing two-hop inference); if the module type is a product module or an evaluation module, relationDepth is set to 1 by default (only allowing direct association); if the riskLevel is high, relationDepth is uniformly reduced to 1 to avoid introducing uncontrollable conclusions from weak relationship inference.

[0077] Step (4): The system calculates claimStrength. Specifically, the system evaluates the average EvidenceScore of the evidence units bound to the current module. If the average EvidenceScore is higher than the first threshold (e.g., 0.75), claimStrength is set to high strength; if it is between the first and second thresholds (e.g., 0.55), claimStrength is set to medium strength; if it is lower than the second threshold, claimStrength is set to low strength. At the same time, if the stage in the target control vector G is the category awareness stage, claimStrength does not exceed medium strength to avoid prematurely outputting strong conclusions before the user has established category awareness.

[0078] Step (5): The system calculates the evidenceLevel. Specifically, the system determines the minimum required level of evidence credibility based on the claimStrength: high-strength conclusions require an audited case or proof; medium-strength conclusions require an evaluation summary or note; and low-strength conclusions allow an evidenceLevel of general knowledge. If the highest credibility level in the evidence unit bound to the current module is lower than the required evidenceLevel, the system downgrades the claimStrength to a level that matches the existing evidence.

[0079] Step (6): The system calculates the exposureLevel. Specifically, the system directly reads the brandExposure value in the target control vector G and maps it to the upper limit of the frequency of brand name appearance: when brandExposure is explicit, the brand name can appear no more than 3 times in the module title and body text; when brandExposure is implicit, the brand name only appears no more than 1 time in the conclusion sentence; when brandExposure is prohibited, the brand name does not appear in the module body text and is replaced by product category or scenario description.

[0080] Through the above six steps, the system calculates differentiated boundary values ​​B′ for different combinations of input parameters such as target control vector G, semantic element type, evidence credibility, and risk level. This ensures that each semantic module is subject to precise field range, relation depth, conclusion strength, evidence requirements, and brand disclosure control during subsequent text generation, avoiding the generation of excessive expressions that exceed the scope of authorized knowledge support or are inconsistent with the current decision-making stage.

[0081] The specific explanation regarding automatic content cropping is as follows: The aforementioned content refers to each sentence or segment generated by filling in the sentence skeleton during the module text generation process. After generating each sentence, the system immediately executes the following cropping judgment process: (i) Field Out-of-Bounds Pruning: The system compares the knowledge fields referenced in the current sentence with the fieldScope one by one. If the sentence references a knowledge field that is not within the fieldScope, the system deletes the expression corresponding to the out-of-bounds field in the sentence; if the sentence is semantically incomplete after deletion, the system replaces it with the closest field within the fieldScope; if no similar field can be found, the entire sentence is marked as out-of-bounds and to be deleted, and replaced with a summary expression at the end of the module.

[0082] (ii) Weak relation trimming: The system checks whether the current sentence contains inferential expressions that exceed the relationDepth hop count. For example, if relationDepth=1, expressions in the sentence that require more than two hops of inference, such as "from this we can infer" or "further related to", are identified and deleted or rewritten as the limiting expression "within the scope of existing knowledge".

[0083] (III) Conclusion Strength Pruning: The system performs a conclusion strength grading on the current sentence and compares it with the upper limit of claimStrength. Specifically, the system maps the words in the sentence to strength levels: those containing words such as "most," "preferred," "unique," and "must" are high-strength; those containing words such as "relatively," "recommended," and "preferred" are medium-strength; and those containing words such as "can," "reference," and "suggestion to consider" are low-strength. If the sentence strength exceeds the upper limit of claimStrength, the system replaces high-strength words with medium-strength or low-strength words. For example, "most suitable for commuting scenarios" is pruned to "relatively suitable for commuting scenarios" or "can be used as a candidate solution in commuting scenarios."

[0084] (iv) Insufficient Evidence Trimming: The system checks whether the current sentence contains a definitive conclusion (such as quantitative data, absolute judgments, or explicit recommendations) and verifies whether the conclusion is covered by evidence units at the evidence level or above. If the coverage is insufficient, the system does not delete the conclusion, but downgrades its expression from definitive to conditional or advisory, and adds an explanation of the applicable boundaries at the end of the sentence, such as adding "in scenarios where knowledge coverage has been maintained" or "based on feedback from existing cases".

[0085] (v) Brand exposure trimming: The system counts the number of times and positions of the brand name appearing in the text generated in the current module. If the cumulative number of times exceeds the exposureLevel limit, the system will replace the brand name in subsequent sentences with the product category name (such as "this tool" or "this product") or scenario description (such as "under such requirements"). If the brand name appears in the module title and the exposureLevel is "prohibited", the system will delete the brand name in the title and only retain the category keyword.

[0086] The method for determining the trimming size: Each trimming is performed on a single sentence or paragraph as the smallest unit. The system does not perform paragraph-level deletion of entire paragraphs, but rather scans and trims sentence by sentence. After trimming, the system recalculates the information completeness of the module text. If trimming results in the loss of core conclusions, the system backtracks to step six to find alternative evidence or backtracks to step three to supplement semantic slots, rather than directly deleting the conclusions. Through the above trimming mechanism, the system ensures that the output text of each module is strictly controlled within the field range, relation depth, conclusion strength, evidence level, and brand exposure limit defined by the boundary value B′, resulting in a refined content control effect that differs from existing technologies.

[0087] During text generation, the system does not directly concatenate bound elements into paragraphs. Instead, it first generates a sentence structure skeleton for each module, consisting of "module intent sentence—fact support sentence—evidence continuation sentence—boundary prompt sentence—transition sentence." Then, it fills in the content according to the allowed fields and dynamic boundary values ​​in the module object. After generation, the system reverse-parses the text into fact items, conclusion items, and limitation items, and verifies them again against the temporary semantic relationship graph and evidence units. Sentences that fail the verification are rewritten, deleted, or transferred to the risk prompt module.

[0088] Step Six: Generate a set of question-and-answer units; the system generates a set of question-and-answer units based on the content theme, user decision-making stage, and semantic module. Each question-and-answer unit includes the question text, answer text, corresponding semantic module, question intent, and applicable stage. The question text can be generated around scenario-based, functional, budget-based, evaluation-based, risk-based, and choice-based questions.

[0089] It should be noted that the question-answering unit can be represented as Q={q,a,intent,stage,module}. Here, q represents the question text, a represents the answer text, intent represents the question intent, stage represents the applicable decision-making stage, and module represents the corresponding semantic module.

[0090] Specifically, the system first establishes a question intent pool based on the user's decision-making stage: during the category awareness stage, scenario-based and choice-based questions are prioritized; during the solution comparison stage, function-based, budget-based, and risk-based questions are prioritized; and during the brand confirmation stage, evaluation-based, proof-based, and action suggestion-based questions are prioritized. The system then reads the module purpose, key elements, and gap elements from the semantic module and converts them into candidate question templates.

[0091] Scenario-based questions are generated by combining usage scenarios, target audiences, and pain points; functional questions are generated by combining product features, applicable tasks, and constraints; budget-based questions are generated by combining price ranges, investment costs, applicable scale, and alternative solutions; evaluation questions are generated by combining evaluation objects, evaluation viewpoints, and evidence summaries; risk-based questions are generated by combining usage restrictions, prohibited expressions, and inapplicable scenarios; and choice-based questions are generated by combining comparison dimensions, decision criteria, and target users. After each candidate question is generated, the system determines whether the question can be supported by at least one semantic module and at least one authorized knowledge field. Candidate questions that cannot be supported are downgraded to questions to be supplemented or directly filtered.

[0092] When generating the answer text, the system organizes the answer in the order of "direct answer - explanation - applicable boundaries - related evidence" and establishes a reference relationship between the answer and the corresponding semantic module, evidence unit and verification rule, thereby avoiding the question and answer unit becoming additional content that is disconnected from the main text.

[0093] It should be noted that the verification rules include: whether the expression of the brand name and product name conforms to the company's rules; whether the question and answer unit is consistent with the semantic module; whether the evidence unit supports the corresponding conclusion; whether the risk warning matches the content theme; and whether the content structure meets the target template.

[0094] The verification rules can be further expressed as: R={scope,object,condition,evidenceRef,action,severity}; In this system, `scope` indicates the scope of application of the rule, `object` indicates the object being checked, `condition` indicates the triggering condition, `evidenceRef` indicates the required evidence or knowledge source, `action` indicates whether to approve, modify, supplement evidence, or prohibit output, and `severity` indicates the risk level. During the check, the system records the location of each rule's hit, the reason for the hit, and the processing result for review by enterprise administrators.

[0095] Furthermore, the quantitative extensions of the above rules are as follows: (1) Semantic module rule extension: The selection of each mi is determined by the ModuleScore score: ModuleScore(m)=p·GoalNeed+q·StageNeed+r·SlotCoverage+t·TemplateFit+z·VisibilityFit; In the formula, p, q, r, t, and z represent the weight coefficients of the five scoring items: GoalNeed, StageNeed, SlotCoverage, TemplateFit, and VisibilityFit, respectively; GoalNeed represents the target requirement matching degree, which measures the degree of matching between the candidate semantic module and the target control vector G; StageNeed represents the decision-making stage requirement matching degree, which measures the degree of adaptation between the candidate module and the current user decision-making stage; SlotCoverage represents the slot coverage degree, which measures the completeness of the candidate module in covering the necessary semantic slots; TemplateFit represents the template adaptation degree, which measures the degree of matching between the candidate module and the module type template; VisibilityFit represents the visibility adaptation degree, which measures whether the candidate module meets the brand exposure level and role permission constraints; and mi represents the i-th semantic module object.

[0096] Among them, the text generation of each mi is constrained by the dynamic boundary value B′. The calculation of B′ is based on the combination of the target control vector G, semantic element type, evidence credibility and risk level; (1) Question answering unit rule extension: the answer a must satisfy the slot closure constraint, the answer is divided into assertions, each assertion must be traced back to the semantic slot_id of the bound module, the matching degree match between the answer assertion and the slot content is not lower than the preset threshold θ_slot, otherwise it is marked as pending (to be supplemented or to be confirmed) and the slot is backed up to be supplemented; (2) Evidence unit rule extension: the supporting relationship between evidence and conclusion is EvidenceScore(e,c)=m·ClaimMat The formula is: ch + n·ModuleMatch + k·Reliability + l·ExpressionFit + h·LimitMatch, where EvidenceScore(e,c) represents the support score of evidence unit e for conclusion c, m, n, k, l, and h represent the weight coefficients of conclusion matching degree, module matching degree, evidence reliability, expression fit degree, and constraint condition matching degree, respectively; ClaimMatch represents conclusion matching degree, ModuleMatch represents module matching degree, Reliability represents evidence reliability, ExpressionFit represents expression fit degree, and LimitMatch represents... The system automatically downgrades the conclusion from a strong expression to a conservative expression when the EvidenceScore is lower than the preset evidence support threshold θ_evidence; (3) The arrangement rule is extended to C=argmax_oArrangeScore(o), with constraint AIFriendly(o)≥θ_ai, where ArrangeScore(o)=A·StageOrder+B·GoalOrder+C·Dependency+D·Readability+E·AIFriendly, and AIFriendly=w_a·HeadingScore+w_b·QAP ArrangeScore(o) represents the arrangement score of candidate arrangement sequence o; A, B, C, D, and E represent the weight coefficients of five scoring items: stage order, target order, dependency, readability, and AI friendliness, respectively. Here, B is the weight symbol, which is distinguished from the module boundary vector B in the context by the position of the formula; w_a, w_b, w_c, w_d, and w_e represent the weight coefficients of title score, question-answer proximity, evidence binding degree, chunk boundary clarity, and meta-information completeness, respectively.StageOrder represents the stage order fit, GoalOrder represents the goal order fit, Dependency represents the dependency satisfaction, and Readability represents the readability score. (4) The verification rule is extended to RepairAction=Map(CheckResult→D→RepairCmd), where D is the deviation correction matrix, used to record the deviation values ​​of each generation unit in terms of field range, evidence level, conclusion strength, module order, and brand exposure level; RepairCmd is the local correction instruction; CheckResult represents the set of verification results; and Map represents the mapping function, used to convert the verification results and the deviation correction matrix D into local correction instructions.

[0097] The aforementioned quantitative rule system forms a complete evaluation chain of "module score selection, boundary value constraint generation, slot closure answer, evidence score adjustment conclusion, arrangement dual-objective optimization, and correction matrix local correction", which enables each link in the content generation process to be quantitatively evaluated and precisely controlled, unlike the conventional methods in existing technologies that lack quantitative evaluation or only perform overall rollback.

[0098] Among them, "direct answer" refers to the first sentence conclusion given in response to the question's intent. The first sentence conclusion must come from the conclusion element in the bound semantic module and must not add any unauthorized facts; "basis explanation" refers to the brand knowledge, product knowledge, scenario elements, or rule elements on which the first sentence conclusion is based, and should correspond to at least one authorized knowledge field; "applicable boundaries" refers to the people, scenarios, conditions, limitations, or inapplicable situations to which the answer is applicable, in order to avoid the answer being interpreted as an unconditional commitment; "related evidence" refers to the evidentiary units that can support the conclusion or evaluation in the answer, including case summaries, evaluation summaries, proof explanations, or notes.

[0099] When generating answers, the system establishes field references for the above four parts: direct answers reference the conclusion field, explanations reference the knowledge field, applicable boundaries reference the restriction or risk field, and related evidence references the evidence unit number. If a question lacks applicable boundaries or related evidence, the system reduces the degree of certainty in the answer, for example, rewriting "suitable for all teams" as "can be used as a candidate solution in scenarios with maintained knowledge coverage"; if the conclusion field required for a direct answer is missing, the question-and-answer unit is marked as to be supplemented and is not included in the final output.

[0100] Step 7: Establish Arrangement Rules; The system establishes arrangement rules based on content objectives and user decision-making stages. In the category awareness stage, the content structure prioritizes an overview module, scenario module, selection criteria module, and Q&A unit, while downplaying the brand module; in the brand confirmation stage, the content structure prioritizes a brand module, product module, evidence unit, Q&A unit, and action suggestion module.

[0101] The specific process is as follows: The system first maps content objectives to structural objectives, such as explanatory, recommendatory, comparative, evidentiary, or conversion objectives; then it maps user decision-making stages to information priorities, such as prioritizing explanations of concepts and scenarios in the cognition stage, prioritizing the display of differences, budgets, and risks in the comparison stage, and prioritizing the display of brands, evidence, and action recommendations in the confirmation stage. Based on the structural objectives and information priorities, the system generates the module order, question-and-answer insertion positions, evidence insertion positions, and brand visibility / implicitness.

[0102] The difference between the above arrangement process and conventional content arrangement methods is that the system does not arrange modules according to a fixed template or manually specified order. Instead, it generates multiple candidate arrangement sequences and calculates an arrangement score (ArrangeScore) and an AI-friendly metric score (AIFriendly) for each candidate sequence. AIFriendly includes five sub-items: HeadingScore (heading structure clarity), QAProximity (proximity of question-answer and binding modules), EvidenceBind (binding tightness of evidence and conclusion), ChunkBoundary (clarity of content chunk boundaries), and MetaCompleteness (meta-information completeness). The system selects the sequence with the highest score and an AIFriendly score not lower than a preset threshold as the final arrangement, thus optimizing both the human reading experience and the generative AI chunk parsing capability.

[0103] The arrangement rules also include constraints: conclusions not bound to evidence units cannot be included in the core conclusion section; risk-related questions and answers must precede the conclusion recommendations; when the brand visibility level is low, the brand module cannot appear before the scenario module; the same evidence unit cannot simultaneously support two contradictory conclusions; when the content objective is comparative, the system must generate a selection criteria module and bind functional, budgetary, and risk-related questions and answers to the corresponding criteria respectively.

[0104] Step 8: Generate AI-Friendly Content; The system combines semantic modules, question-and-answer units, and evidence units into structured content according to arrangement rules. The arrangement rules can be represented as C=order(M,Q,E,stage,goal). The system determines the module order based on the user's decision-making stage and content objectives. The output includes a title, content summary, core conclusions, modular body text, question-and-answer paragraphs, evidence paragraphs, risk warnings, and conclusion suggestions. Furthermore, the combination is not a simple splicing process. The system first generates a content skeleton, including paragraph levels, module order (i.e., the insertion order mentioned above), question-and-answer slots, evidence slots, and conclusion slots. Then, according to the arrangement rules, semantic modules are written into their corresponding body text slots, question-and-answer units are written into question-and-answer slots adjacent to their corresponding modules, and evidence units are written into evidence slots that support the corresponding conclusions or evaluation viewpoints.

[0105] The difference between the above-described generation process and the conventional paragraph splicing method lies in the following: the system embeds elements step by step according to the unit type of the arrangement sequence. When encountering a semantic module, it outputs an H2-level title plus a four-sentence structure of the module body (core conclusion sentence, factual supporting sentence, applicable boundary sentence, and transition sentence). When encountering a question-and-answer unit, it inserts a question-and-answer block with a question tag after its bound module. When encountering an evidence unit, it inserts an evidence block within a distance of no more than δ=2 units after the conclusion corresponding to its target_claim. During the embedding process, the system performs context connection processing on each slot and inserts structured delimiters, enabling the generative AI to perform block parsing and extraction according to the tags, rather than outputting all content as unstructured continuous text. Here, H2 represents the second-level title level, used to carry the semantic module title; target_claim represents the target conclusion pointed to and supported by the evidence unit; and δ represents the nearest distance threshold for evidence.

[0106] The specific explanation of paragraph hierarchy and module order is as follows: Paragraph hierarchy refers to the structured hierarchical division of the output content, which includes four levels from the outside to the inside: The first level is the document level, which includes eight top-level paragraphs: title, introduction, core conclusion, modular body, Q&A paragraph, evidence paragraph, risk warning, and conclusion recommendation; The second level is the module level, where each semantic module in the modular body constitutes an independent paragraph block, starting with an H2 level heading, followed by the module's core conclusion sentence, supporting explanation sentence, applicable boundary sentence, and transition sentence; The third level is the sentence / paragraph level, where the text within each module is organized according to a fixed sentence / paragraph order of "conclusion, support, boundary, transition"; The fourth level is the element level, where specific facts, data, brand names, product names, etc., in each sentence / paragraph are filled in by authorized knowledge fields.

[0107] The module order refers to the arrangement of semantic modules, question-and-answer units, and evidence units in the arrangement sequence O. The module order is determined according to the following rules: Rule 1: Stage Priority Rule. The system retrieves the basic sequence from the stage template library based on the user's decision-making stage. The basic sequence for the category awareness stage is [Overview Module, Scenario Module, Selection Criteria Module, Q&A Unit, Conclusion and Suggestion Module]; the basic sequence for the solution comparison stage is [Overview Module, Comparison Module, Evaluation Module, Evidence Unit, Q&A Unit, Risk Warning Module, Conclusion and Suggestion Module]; the basic sequence for the brand confirmation stage is [Overview Module, Brand Module, Product Module, Evidence Unit, Q&A Unit, Action Suggestion Module, Conclusion and Suggestion Module].

[0108] Rule 2: Proximity Rule. Each piece of evidence in sequence O must be located after the conclusion sentence to which its target_claim is bound, and within a distance of δ = 2 other units. If the interval between an evidence unit and its bound conclusion in the base sequence exceeds δ units, the system will move the evidence unit to the nearest position that satisfies the distance constraint. Sequence O, in Chinese, is the content unit arrangement sequence, used to record the arrangement positions of semantic modules, question-and-answer units, evidence units, and boundary hint units in the final text; target_claim represents the target conclusion corresponding to the evidence unit; δ represents the proximity threshold for evidence, used to limit the maximum interval between an evidence unit and its supporting conclusion.

[0109] Rule 3: Question-Answer Following Rule. Each question-answer unit in sequence O must immediately follow the semantic module to which its module_id is bound. If a semantic module is associated with multiple question-answer units, they are arranged in descending order of QuestionScore. module_id represents the semantic module number and is used to determine the order of multiple question-answer units within the same semantic module, based on question intent matching, decision-stage fit, slot coverage, and evidence answerability.

[0110] Rule 4: Risk Pre-emption Rule. The risk warning module and all risk-related Q&A units must be placed before the conclusion and suggestion module in sequence O to ensure that users understand the usage limitations and risks before seeing the final advice.

[0111] Rule 5: Brand Exposure Rule. When brandExposure in the target control vector G is implicit or prohibited, the brand module must not be positioned earlier than the scene module and product module in sequence O; when brandExposure is prohibited, the brand module is removed from sequence O, and its content is distributed to the transition sentences of other modules.

[0112] After the system generates candidate orchestration sequences, it calculates the ArrangeScore and AIFriendly score for each candidate sequence, and selects the sequence with the highest score that meets the above five rules as the final module order.

[0113] During the writing process, the system performs contextual processing for each slot, including standardizing terminology, product names, and brand names, eliminating duplicate conclusions, adding transitional sentences, and restricting expansion without evidence. When the same semantic module is associated with multiple question-and-answer units or evidence units, the system determines the display order based on the user's decision-making stage, the credibility of the evidence, and the coverage of the questions. Content that cannot be supported by evidence units can only be output as general explanations and cannot be output as definitive conclusions or quantitative commitments.

[0114] Step Nine: Perform Brand Expression and Content Rule Verification; The system verifies the generated content based on the brand expression and content rules in the enterprise space. Verification includes checking for prohibited expressions, unfounded parameterized expressions, expressions inconsistent with brand knowledge, consistency between the Q&A section and the main text module, and correspondence between the evidence section and the conclusion module.

[0115] The specific verification process includes structural verification, factual verification, evidence verification, Q&A consistency verification, brand rule verification, and risk verification. Structural verification determines whether the title, abstract, core conclusions, modular body text, Q&A sections, evidence sections, and risk warnings are complete and in the correct order. Factual verification compares the brand name, product name, function, parameters, applicable scenarios, and limitations with the authorized knowledge package item by item. Evidence verification determines whether each definitive conclusion has a corresponding evidence unit and whether the scope of support for that evidence unit covers the conclusion.

[0116] The question-answer consistency check determines whether the intent of the question is consistent with the bound semantic module, and whether the facts and conclusions in the answer have appeared in the main text module or evidence unit; the brand rule check detects prohibited words, brand visibility level, tone and style, competitor comparison boundaries and compliance prompts; the risk check detects absolute terms, unfounded quantitative expressions, exaggerated effects, omission of limiting conditions, and inconsistencies between evaluation materials and conclusions.

[0117] After verification, the system generates a status identifier for each verification result, including "Passed," "Requires Rewriting," "Requires Supplementary Evidence," and "Output Prohibited." For content requiring rewriting, the system automatically replaces it with a conservative expression according to the corresponding rules; for content requiring supplementary evidence, the system backtracks to the evidence unit or authorized knowledge package to find supporting materials; for content prohibited from output, the system deletes the relevant expression and records the reason for deletion in the verification result. Only after all mandatory verification items have passed or been processed will the system output the final result.

[0118] The difference between the above-mentioned verification-driven mechanism and conventional overall rollback or manual revision methods lies in the following: The system maps the verification results to a correction deviation matrix D. Each non-zero element in D corresponds to a local correction instruction, RepairCmd. RepairCmd only rewrites the downstream nodes of target_unit and unit_graph, taking priority over the entire rollback. The verification iteration does not exceed a preset upper limit N_max. If it fails, it rolls back to the corresponding step according to feedback_step, thereby achieving local precise correction rather than full text regeneration and reducing the risk of structural drift. Here, target_unit represents the target content unit to be corrected, unit_graph represents the content unit dependency graph, N_max represents the maximum number of verification iterations, and feedback_step represents the target step number that needs to be rolled back when the verification fails. Through the combination of the correction deviation matrix D and the content unit dependency graph unit_graph, the system only rewrites the target unit and its downstream affected units, without full text regeneration. In one embodiment, when the evidence level is lower than evidenceLevel, the corresponding conclusion is only downgraded from a deterministic recommendation to a recommendation that can be referenced under limited conditions, and the adjacent evidence continuation sentences are updated simultaneously.

[0119] Step 10: Output Content Results; The system outputs AI-friendly content results, including structured articles, question-and-answer modules, summary modules, title options, content structure templates, and verification results.

[0120] The above-mentioned technical solution of the present invention will be further described below with reference to the embodiments, specifically including: Example 1: Generation of coffee product recommendation content; The enterprise space maintains knowledge about a specific coffee brand and product, including brand positioning, product features, applicable scenarios, user reviews, and case studies. The content theme is "Recommended Coffees for Commuting." The system identifies scenario elements, product elements, target audience elements, evaluation elements, and conclusion elements, and generates overview modules, scenario modules, product modules, evaluation modules, and conclusion / suggestion modules.

[0121] The system generates question-and-answer units, such as, "What type of coffee is suitable for commuting?" The answer is generated based on authorized knowledge within the enterprise space and is consistent with the scenario and product modules. The system also generates evidence units, such as summarizing typical user feedback on taste and convenience based on user review materials maintained within the enterprise space. Finally, it outputs a structured article with clear titles, summaries, scenario descriptions, question-and-answer paragraphs, and evidence paragraphs.

[0122] Example 2: Generating content for enterprise SaaS tool selection; The enterprise space maintains brand and product knowledge for a specific SaaS tool, including product features, applicable teams, customer cases, user reviews, and usage limitations. The content theme is "How to Choose Enterprise Content Operation Tools." The system identifies semantic elements such as features, teams, budget, scenarios, reviews, and risks.

[0123] The system generates an overview module, a scenario module, a product module, a comparison module, a question-and-answer unit, an evidence unit, and a conclusion / suggestion module. Through this embodiment, the system can generate AI-friendly content with a clear structure from a knowledge base and content topics, rather than directly outputting a generic marketing copy.

[0124] Example 3: Rule-based content generation in enterprise spaces; Enterprise administrators configure different brand knowledge bases, product knowledge bases, and content rules for different departments. When department A generates content for brand A, the system only uses the knowledge and rules authorized for department A and outputs the content according to the content template configured for that department. For ordinary members, the system allows them to generate content drafts but restricts their modification of evidence units and brand rules; for administrators, the system allows them to adjust semantic module templates, question-and-answer unit rules, and verification rules.

[0125] In addition, the present invention also provides a computer-readable storage medium.

[0126] In this optional embodiment, a computer program is stored on the computer-readable storage medium, which, when executed by a processor, implements the steps of the above method.

[0127] In addition, the present invention also provides a computer program product.

[0128] In this optional embodiment, the computer program product stores a computer program that, when executed by a processor, implements the steps of the method.

[0129] This invention is not limited to the structures described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope. The scope of this invention is limited only by the appended claims.

Claims

1. A method for generating AI-friendly content, characterized in that, Includes the following steps: The content theme and generation objective are obtained, and the generation objective is converted into an objective control vector. Based on the objective control vector, the authorized knowledge within the enterprise space is read to form an authorized knowledge package. Based on the content theme and authorized knowledge package, semantic elements are identified, and sets of semantic modules, question-and-answer units, and evidence units are generated respectively. When generating semantic modules, a dynamic boundary trimming mechanism is used to generate boundary values ​​for each semantic module based on the target control vector, semantic element type, evidence credibility, and risk level. Multi-dimensional trimming is performed based on the boundary values ​​when generating text sentence by sentence. Based on the user decision-making stage and semantic elements in the target control vector, an orchestration rule is established to generate multiple candidate orchestration sequences. An orchestration score and an AI-friendly metric score are calculated for each candidate sequence. The candidate orchestration sequence with the highest orchestration score and an AI-friendly metric score not lower than a preset score threshold is selected as the target orchestration sequence. Based on the target arrangement sequence, the semantic module set, question-and-answer unit set, and evidence unit set are combined to generate structured content. The structured content is then checked and corrected in conjunction with the brand expression rules and content rules in the enterprise space to generate AI-friendly content.

2. The AI-friendly content generation method according to claim 1, characterized in that, The process of acquiring the content theme and generation target, converting the generation target into a target control vector, and reading authorized knowledge within the enterprise space based on the target control vector to form an authorized knowledge package includes the following steps: It receives the content theme and generation target from the content deployment plan, and transforms the generation target into a target control vector through preset conversion rules; Based on the permission indication information in the target control vector, combined with the current user's role and permissions in the enterprise space, the scope of authorization is determined, and within the scope of authorization, candidate knowledge is retrieved according to the content topic; Candidate knowledge is reviewed and verified, and candidate knowledge that fails the review and verification is removed to form an authorized knowledge package.

3. The AI-friendly content generation method according to claim 1, characterized in that, The step of identifying semantic elements based on the content topic and authorized knowledge package, and generating a semantic module set, a question-answering unit set, and an evidence unit set respectively, includes the following steps: The content topic is processed by word segmentation, intent recognition and scene classification, and knowledge fields are extracted from the authorized knowledge package. After synonym normalization and entity alignment, semantic elements are identified. Identify the types of materials in the authorized knowledge package and remove duplicates from the materials in the authorized knowledge package to obtain a set of evidence units; Based on semantic elements, a set of semantic modules is generated through module candidate determination, element binding, boundary verification, and text generation operations, combined with a dynamic boundary clipping mechanism. Based on the user decision-making stage in the target control vector, a corresponding question intent pool is established, and key elements are extracted from the semantic module set to be converted into candidate question templates, generating question text, and answer text is generated according to a four-segment structure to obtain a question-answer unit set.

4. The AI-friendly content generation method according to claim 3, characterized in that, The process of identifying the material types in the authorized knowledge package and deduplicating the materials in the authorized knowledge package to obtain the evidence unit set includes the following steps: Identify the types of materials included in the authorized knowledge package, such as case materials, evaluation materials, and supporting materials; Based on the material type, each material is sequentially subjected to deduplication processes including structural deduplication, semantic deduplication, and evidence deduplication, and source verification is performed on the deduplicated materials. For materials that pass deduplication and source verification, sensitive information filtering is performed. Based on the material type, corresponding evidence units are generated, and a source number and credibility identifier are written into each evidence unit to form an evidence unit set.

5. The AI-friendly content generation method according to claim 3, characterized in that, The process of performing word segmentation, intent recognition, and scene classification on the content topic, and extracting knowledge fields from the authorized knowledge package, and identifying semantic elements after synonym normalization and entity alignment, includes the following steps: The content topic is segmented to obtain core topic words, limited topic words, and risk topic words, and the user's current idea graph and candidate intent are identified; The core keywords, limited keywords, and risk keywords are matched with the preset scenario tag tree in the enterprise space. The content topics are then classified into scenarios by calculating semantic similarity, field hit rate, and evidence coverage, and the scenario classification results are obtained. Based on the scenario classification results, evaluation materials and case materials related to the scenario classification results are retrieved from the authorized knowledge package, and reverse evidence annotation is performed on the evaluation materials and case materials to generate limiting elements, applicable boundary elements and elements to be verified. Based on the idea graph and candidate intent, the corresponding entity attribute fields are extracted from the authorized knowledge package. The entity attribute fields are normalized using a pre-set thesaurus. The normalized entity attribute fields are then aligned with the standard entity library in the enterprise space to generate standardized semantic features. The standardized semantic features are integrated with the limiting elements, applicable boundary elements, and elements to be verified to obtain semantic elements.

6. The AI-friendly content generation method according to claim 3, characterized in that, The process of generating a semantic module set based on semantic elements, through module candidate determination, element binding, boundary verification, and text generation operations, combined with a dynamic boundary clipping mechanism, includes the following steps: Based on the user decision-making stage in the content theme and target control vector, semantic module candidate types are determined from the preset module type library, and the identified semantic elements are bound to the corresponding semantic module candidate types according to the semantic module types to generate initial semantic module objects; Perform boundary validation on the initial semantic module object, and use the initial semantic module object that passes the boundary validation as the final semantic module object; By reading the brand visibility level, verification rigor, and user decision-making stage from the target control vector, and combining the type of semantic module object, the set of bound semantic elements, and the credibility of evidence, the dynamic boundary value of each final semantic module object is calculated. The sentence skeleton is generated based on the final semantic module object, and the content of the sentence skeleton is filled according to the dynamic boundary value to generate the initial module text. The initial module text is reverse-parsed into fact items, conclusion items, and restriction items, and then verified against the temporary semantic relationship graph and evidence units. The initial module text that passes the verification is used as the final semantic module set.

7. The AI-friendly content generation method according to claim 5, characterized in that, The boundary checks include: field boundary checks, relationship boundary checks, stage boundary checks, and evidence boundary checks; The field boundary check is used to determine whether the knowledge field referenced by the initial semantic module belongs to the set of fields allowed by the initial semantic module. The relation boundary verification is used to determine whether there is a valid path between brands, products, scenarios, evaluations and evidence within the initial semantic module in the temporary semantic relation graph; The stage boundary verification is used to determine whether the initial semantic module expression conforms to the user decision stage in the target control vector; The evidence boundary check is used to determine whether the deterministic conclusion in the initial semantic module is covered by the corresponding evidence unit.

8. The AI-friendly content generation method according to claim 6, characterized in that, The process of calculating the dynamic boundary value of each final semantic module object by reading the brand visibility level, verification rigor, and user decision-making stage from the target control vector, combined with the type of the semantic module object, the set of bound semantic elements, and the credibility of the evidence, includes the following steps: Read the module type and the set of bound semantic elements of the current final semantic module object, and extract the brand visibility level, verification rigor and user decision stage from the target control vector; By querying the allowed field template corresponding to the current module type, and taking the intersection of the field set of the field template and the knowledge field corresponding to the bound semantic element, the prohibited fields and fields outside the role permissions hit by the target control vector are removed to obtain the field range boundary; By statistically analyzing the average path length of the core entities of the current final semantic module object in the temporary semantic relationship graph, and combining the module type and the strictness of verification, the maximum number of hops for semantic relationship inference is determined. The average evidence score of the evidence unit bound to the current final semantic module object is evaluated, and the upper limit level of the conclusion expression strength is obtained by combining the two-level threshold judgment. The minimum evidence credibility level required is determined in reverse based on the upper limit level of the conclusion expression strength. By mapping the brand visibility level in the target control vector to the upper limit of the frequency of the corresponding brand name, the brand visibility boundary is obtained. Then, by integrating the field range boundary, the maximum number of hops inferred from semantic relationships, the upper limit of the conclusion expression strength level, the minimum evidence credibility level, and the brand visibility boundary, the dynamic boundary value of the final semantic module object is obtained.

9. The AI-friendly content generation method according to claim 3, characterized in that, The process of establishing a corresponding question intent pool based on the user decision-making stage in the target control vector, extracting key elements from the semantic module set to convert them into candidate question templates, generating question text, and generating answer text according to a four-segment structure to obtain a question-answer unit set includes the following steps: Based on the user decision-making stage in the target control vector, determine the priority of the question intent and establish a corresponding question intent pool; The module purpose, key elements, and gap elements of each semantic module are read from the generated set of semantic modules. Combined with the question intent pool, candidate question templates are generated and then filtered to obtain a set of question texts. For each question in the question text set, generate the answer text according to the four-segment structure, and perform slot closure verification on the answer to obtain the question-answer unit set.

10. The AI-friendly content generation method according to claim 8, characterized in that, The step of establishing orchestration rules based on the user decision-making stage and semantic elements in the target control vector to generate multiple candidate orchestration sequences, calculating an orchestration score and an AI-friendly metric score for each candidate sequence, and selecting the candidate orchestration sequence with the highest orchestration score and an AI-friendly metric score not lower than a preset score threshold as the target orchestration sequence includes the following steps: Content objectives are mapped to structural objectives, user decision-making stages are mapped to information priorities, and sequence constraints, question-and-answer insertion position constraints, evidence insertion position constraints, and brand explicitness / implicitness constraints are generated based on structural objectives and information priorities to form arrangement rules. Based on the orchestration rules, multiple candidate orchestration sequences are generated, and an orchestration score and an AI-friendly metric score are calculated for each candidate orchestration sequence. All candidate sequences are sorted from highest to lowest according to their arrangement scores. The candidate arrangement sequence with the highest arrangement score and an AI-friendly quantification score not lower than a preset score threshold is selected and determined as the final target arrangement sequence.

11. The AI-friendly content generation method according to claim 1, characterized in that, The process of combining semantic module sets, question-and-answer unit sets, and evidence unit sets to generate structured content based on the target arrangement sequence, and then verifying and correcting the structured content in conjunction with brand expression rules and content rules in the enterprise space to generate AI-friendly content includes the following steps: Construct a content skeleton that includes paragraph hierarchy, insertion order, question and answer slots, evidence slots, and conclusion slots. Embed semantic modules, question and answer units, and evidence units into the corresponding slots step by step according to the target arrangement sequence, and insert structured delimiters to generate initial structured content. Multiple checks are performed sequentially on the initial structured content, and a status identifier is generated for each check result. Based on the status identifier, the initial structured content that fails the verification is corrected, and the corrected structured content is used as AI-friendly content.

12. The AI-friendly content generation method according to claim 11, characterized in that, The multiple checks include: structural checks, factual checks, evidence checks, consistency checks of questions and answers, brand rule checks, and risk checks.

13. An AI-friendly content generation system, characterized in that, The system includes: The authorized knowledge acquisition module is used to acquire content topics and generation objectives, convert the generation objectives into target control vectors, and read authorized knowledge within the enterprise space based on the target control vectors to form an authorized knowledge package; The content unit construction module is used to identify semantic elements based on the content theme and authorized knowledge package, and generate a set of semantic modules, a set of question-and-answer units, and a set of evidence units respectively. When generating semantic modules, a dynamic boundary pruning mechanism is used to generate boundary values ​​for each semantic module based on the target control vector, semantic element type, evidence credibility, and risk level. Multi-dimensional pruning is performed based on the boundary values ​​when generating text sentence by sentence. The content orchestration module is used to establish orchestration rules based on the user decision-making stage and semantic elements in the target control vector, generate multiple candidate orchestration sequences, calculate orchestration score and AI-friendly metric score for each candidate sequence, and select the candidate orchestration sequence with the highest orchestration score and AI-friendly metric score not lower than the preset score threshold as the target orchestration sequence. The content generation and verification module is used to combine the semantic module set, question and answer unit set, and evidence unit set to generate structured content according to the target arrangement sequence. It also verifies and corrects the structured content in combination with the brand expression rules and content rules in the enterprise space to generate AI-friendly content.

14. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 12.

15. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 12.

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