Method and system for intelligent content generation and multi-platform automated publishing

Abstract

This invention relates to the fields of computer and artificial intelligence technologies, and discloses an intelligent content generation and multi-platform automated publishing method and system. The method calls a real-time keyword data interface to acquire data and generates focus and secondary keywords based on semantics and search intent; it applies preset parameters according to the selected content purpose and assembles structured prompts, triggering an AI content workflow to generate content and accompanying images; it automatically identifies the content specifications of the target platform through a platform adapter, including character limits, image specifications, format types, and metadata fields of the content management system, and performs format conversion, word adaptation, and field semantic mapping on the content and accompanying images accordingly, obtaining data to be published that conforms to the platform's specifications and submitting it for publication; when adding a new platform, only the corresponding adapter needs to be expanded to adapt to its specifications. This invention achieves full automation from keyword to multi-platform publishing and automatic, precise adaptation to different platforms.

Description

Specification 1 Intelligent Content Generation and Multi-Platform Automated Publishing Method and System Technical Field This invention relates to the fields of computer and artificial intelligence technology, specifically, to an intelligent content generation and multi-platform automated publishing method and system. Background Technology With the development of online marketing and search engine optimization (SEO), enterprises need to continuously produce a large amount of content that is relevant to their target audience and has search value, and distribute it to multiple platforms such as websites, blogs, and social media. Traditional manual SEO typically includes the following steps: manually researching keywords using tools to find relevant keywords commonly used by the target audience in search engines; having experienced personnel write high-quality and valuable content; manually checking website technical issues, such as page loading speed and mobile-friendliness; and manually interpreting and analyzing data generated by tools. These steps are highly dependent on human experience, and the process is slow and inefficient. Furthermore, after content is produced, staff often need to manually copy and paste the content to various social media platforms or websites, which is time-consuming and prone to errors. While related technologies offer some solutions for content generation using artificial intelligence, their multi-platform publishing often involves simple text copy-pasting or publishing the same content to different platforms via Application Programming Interfaces (APIs). This fails to provide fine-grained adaptation to the content format requirements, word limits, image specifications, and terminology policies of different platforms. Particularly when publishing to Content Management Systems (CMS), the inability to accurately set metadata such as article categories, layouts, and types leads to misaligned information architecture in the destination system, resulting in only text presentation without proper layout and structure. Furthermore, the content production processes of these technologies are often fixed, making it difficult to dynamically adjust the generation and publishing steps according to different content uses. They also lack consistency constraints between the front-end interface and the back-end workflow, easily leading to structural inconsistencies such as fields filled in on the interface not being received on the back-end, or required fields not being displayed on the interface. Moreover, when integrating with new publishing platforms, the entire generation logic often needs to be rewritten, resulting in high expansion costs. It should be noted that the information disclosed in the background section above is only used to enhance the understanding of the background of this invention. Summary of the Invention The purpose of this invention is to provide an intelligent content generation and multi-platform automated publishing method and system, thereby overcoming, to at least a certain extent, the problems of rough multi-platform publishing adaptation, fixed production processes, and high platform expansion costs in related technologies, achieving full-process automation from keywords to multi-platform publishing, as well as intelligent and precise adaptation to different platforms.According to one aspect of the present invention, a method for intelligent content generation and automated multi-platform publishing is provided, comprising: calling a real-time keyword data interface to obtain keyword data, and grouping it according to semantics and search intent to generate focus words and secondary words; applying preset parameters and assembling structured prompts according to the selected content purpose to trigger an AI content workflow to generate content, and generating accompanying images based on the content; automatically identifying the content specifications of the target platform through a platform adapter, the content specifications including word count limits, image specifications, format types, and metadata fields of the content management system; performing format conversion, word adaptation, and field semantic mapping on the identified content specifications and accompanying images to obtain data to be published that conforms to the specifications of the target platform and submitting it for publication; when a new platform is added, expanding the corresponding platform adapter to identify and adapt to the content specifications of the new platform. In some embodiments of the present invention, based on the foregoing scheme, the step of grouping and generating focus words and secondary words according to semantics and search intent includes grouping keywords according to semantic similarity and search intent, with each group generating one focus word and 3-4 secondary words; the real-time keyword data interface is a search volume query service interface or a keyword planning service interface. In some embodiments of the present invention, based on the foregoing scheme, the method further includes topic planning: generating candidate topics using a language model based on the selected content purpose, language, and region, wherein the candidate topics include a title, keywords, and description, and storing the selected candidate topics as topic suggestions; when generating the content of HK 30137923 A Specification 3, filtering the topic suggestion list based on the selected content purpose, and backfilling the title, keywords, and description of the selected topic suggestion into the content generation form when a topic suggestion is selected. In some embodiments of the present invention, based on the foregoing scheme, applying preset parameters according to the selected content purpose includes: applying preset parameters from a preset value lookup table based on the selected content purpose, wherein the preset parameters include language, region, tone, writing style, word count, and audience; when the selected content purpose is website-related, displaying a FAQ block and including the FAQ in the request carried to the backend, so that the set of fields visible on the interface is consistent with the set of fields that can be parsed by the backend. This makes the content purpose not only a tag, but also an index of the parameter space and an isomorphic constraint of the front-end and back-end fields, reducing structural inconsistencies. In some embodiments of the present invention, based on the foregoing scheme, the method further includes dynamically determining the process steps according to the purpose of the selected content: when the purpose of the selected content is a type that does not require an image, the process steps are content generation, content checking and adjustment, and preview or upload; otherwise, an image generation step is inserted into the process steps.In some embodiments of the present invention, based on the foregoing scheme, the AI ​​content workflow branches conditionally based on the selected content purpose. Each branch uses a prompt template and processing flow specific to that content purpose, and generates content using a retrieval-enhanced generation method in conjunction with a knowledge base. The accompanying image is generated by a diffusion model, and the prompt words are optimized or expanded using a language model before being fed into the diffusion model. In some embodiments of the present invention, based on the foregoing scheme, the submission and publication includes submission in the form of immediate publication, scheduled publication, or saving as a draft. The content management system is configured with article status, format, article or page type, and category, and the available publishing platform is determined according to the category. In some embodiments of the present invention, based on the foregoing scheme, the method further includes adaptive learning: recording user editing behavior on the generated content, the editing behavior including modifications to style, structure, content, and tone, storing the editing behavior in a user preference library, and adjusting the generation parameters or model weights accordingly. According to another aspect of the present invention, an intelligent content generation and multi-platform automated publishing system is provided, comprising: a keyword research and grouping module, used to call a real-time keyword data interface to obtain keyword data, and group it according to semantics and search intent to generate focus words and secondary words; a content generation module, used to apply preset parameters according to the selected content purpose and assemble structured prompts to trigger an AI content workflow to generate content; a design generation module, used to generate accompanying images according to the content; and a multi-platform automated publishing module, which is equipped with a platform adapter, used to automatically identify the content specifications of the target platform and perform format conversion, word adaptation and column semantic mapping accordingly, to obtain data to be published that conforms to the specifications of the target platform and submit it for publication, and when a new platform is connected, the corresponding platform adapter is expanded to identify and adapt to the content specifications of the new platform. According to another aspect of the present invention, a computer-readable storage medium is also provided, on which a computer program is stored, which, when executed by a processor, implements the above-described intelligent content generation and multi-platform automated publishing method. According to one aspect of the present invention, an electronic device is also provided, including a processor and a memory for storing processor-executable instructions, the processor being configured to perform the above-described intelligent content generation and multi-platform automated publishing method by executing the executable instructions.As can be seen from the above technical solutions, the intelligent content generation and multi-platform automated publishing method and system in the exemplary embodiments of the present invention have at least the following advantages and positive effects: First, it achieves full-process automation from real-time keyword analysis, theme planning, content generation, image generation to multi-platform publishing, and provides an entry point for one-click pre-filling and uploading of generated content and images, significantly shortening the time from content production to publishing and improving efficiency; Second, it automatically identifies the content specifications of the target platform through the platform adapter and carries the content with column semantic mapping, enabling format conversion, word policy adaptation, and fine-tuning of metadata for different platforms, achieving fine-tuning of cross-platform content and consistency of information architecture. Moreover, when a new platform is added, only the corresponding platform adapter needs to be expanded, without rewriting the generation core, thereby greatly reducing the platform expansion cost; Third, it drives the preset parameter comparison and conditional interface with the content purpose, and keeps the set of visible columns on the interface and the set of parsable columns on the backend isomorphic, combined with dynamically determining the process steps according to the purpose, reducing structural inconsistencies and improving process clarity and flexibility; its HK 30137923 A specification 5 Fourth, by recording user editing behavior through adaptive learning and optimizing generation accordingly, combined with knowledge base retrieval to enhance generation and provide factual constraints, the output content becomes more personalized and professional. The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with the present invention and, together with the description, serve to explain the principles of the invention. In the drawings: Figure 1 shows a schematic diagram of the overall architecture of an intelligent content generation and multi-platform automated publishing system to which embodiments of the present invention can be applied; Figure 2 schematically shows a flowchart of an intelligent content generation and multi-platform automated publishing method according to an embodiment of the present invention; Figure 3 schematically shows a flowchart of multi-platform automatic identification and contractual adaptation publishing according to an embodiment of the present invention; Figure 4 schematically shows a flowchart of content usage-driven preset parameter comparison and dynamic steps according to an embodiment of the present invention; Figure 5 schematically shows a flowchart of adaptive learning closed loop and knowledge base retrieval constraints according to an embodiment of the present invention. Detailed Description Exemplary embodiments will now be described more fully with reference to the accompanying drawings. However, exemplary embodiments can be implemented in many forms and should not be construed as limited to the examples set forth herein; rather, these embodiments are provided to make the invention more comprehensive and complete, and to fully convey the concept of exemplary embodiments to those skilled in the art. Numerous specific details are provided in the following description to give a full understanding of the embodiments of the invention; however, those skilled in the art will recognize that the technical solutions of the invention may be practiced with one or more of the specific details omitted, or other methods, components, apparatuses, steps, etc. may be employed.It should be noted that Artificial Intelligence (AI) is the theory, methods, technology, and application system that uses digital computers or machines controlled by digital computers to simulate, extend, and expand human intelligence, perceive the environment, acquire knowledge, and use that knowledge to obtain optimal results. Machine Learning (ML) is the core of AI, specifically studying how computers can simulate or implement human learning behavior to acquire new knowledge or skills. Natural Language Processing and generative models are important branches of AI, applicable to tasks such as text understanding and generation, and image generation. The solutions provided in this invention involve AI-based content generation and multi-platform automated publishing technologies, which are specifically illustrated in the following embodiments. Figure 1 shows a schematic diagram of the overall system architecture applicable to embodiments of this invention. As shown in Figure 1, the intelligent content generation and multi-platform automated publishing system 100 includes: a real-time keyword data interface 110, a keyword research and grouping module 120, a content generation module 130, a knowledge base 140, a design generation module 150, an adaptive learning module 160, and a multi-platform automated publishing module 170, wherein the multi-platform automated publishing module 170 is equipped with a platform adapter 171. The system 100 connects to multiple target platforms via the multi-platform automated publishing module 170, including but not limited to WordPress 181, Facebook 182, Instagram 183, and Xiaohongshu 184. It should be understood that the number and division of the above modules are only illustrative and can be merged or split according to implementation needs. The front end can use single-page or multi-page applications to implement dashboards, theme planning, step navigation, forms and lists, etc.; the back end is responsible for user authentication, receiving generation requests and triggering AI content workflows, writing generation results into the database, and handling publishing and scheduling, etc. Figure 2 schematically shows a method flowchart according to an embodiment of the present invention. Referring to Figure 2, the method includes steps S110 to S160. Step S110: Keyword data is obtained by calling the real-time keyword data interface 110, and grouped according to semantics and search intent to generate focus words and secondary words. Step S120: Topic planning is performed, generating and storing topic suggestions. Step S130: Preset parameters are applied according to the selected content purpose, and structured prompts are assembled to trigger the AI ​​content workflow to generate content. If necessary, the knowledge base 140 is used for enhanced retrieval. Step S140: Design generation is performed to produce accompanying images.Step S150: The platform adapter 171 identifies the content specifications of the target platform and performs format conversion, terminology adaptation, and field semantic mapping. Step S160: The data to be published is submitted to the target platform in either immediate, scheduled, or draft status. For step S110, the keyword research and grouping module 120 can obtain keywords from various sources, including: calling the search volume query service interface or keyword planning service interface, where the user inputs seed keywords and sets the language and target region, and the backend queries and returns an expanded keyword list, which may include fields such as average monthly search volume and competition level; uploading a CSV or Excel file and specifying keyword fields; manual input; and product or service-related keywords returned by the backend as a quick selection menu. Module 120 uses natural language processing to determine the semantic similarity and search intent (e.g., informational, commercial, navigational) of keywords. Under the constraints of a grouping algorithm or rule engine, it groups the keywords into several semantically consistent groups. Each group outputs a focus keyword and 3-4 secondary keywords, serving as the input basis for SEO elements and subsequent topic generation and content generation forms. For step S120, topic planning is performed before the promotion process to generate topic suggestions for the content generation form. The backend uses a language model to assemble suggestions based on parameters such as main keyword, content purpose (multiple selections allowed), language, region, and the number generated for each purpose. It can trigger the AI ​​content workflow in batches and then summarize and return candidate topics. Each candidate topic can include a title, main keyword and search volume, secondary keywords, important information, and the purpose of the content. Users can save selected candidate topics as topic suggestions, which the system persists in a topic suggestion library format (including title, description, and keyword list). The content generation form requests a list of topic suggestions from the backend through the topic suggestion selection interface, and can filter them according to the purpose of the currently selected content to avoid misuse across purposes. After selection, the title, keywords, and description are populated back into the form fields such as title, primary keyword, secondary keyword, and important information. Figure 4 schematically illustrates the flowchart of the preset parameter comparison and dynamic steps driven by content purpose.Referring to Figure 4, in step S410, the user selects the purpose of the content; in step S420, the system applies preset parameters for the content purpose from a preset value lookup table. These preset parameters include language, region, tone, writing style, word count, and target audience. Then, it determines whether the selected content purpose is website-related. If so, a FAQ section is displayed, and the FAQs are included in the request sent to the backend, ensuring that the visible field set on the interface is isomorphic to the parsable field set on the backend. If not, the FAQ section is hidden and not sent. Further, it determines whether an image is needed. If the selected content purpose is a type that does not require an image (e.g., email, video script, video platform SEO), the process consists of three steps: content generation, content checking and adjustment, and preview or upload. Otherwise, the image generation step constitutes four steps. In step S430, a dynamic step array is output for frontend navigation and navigation. Thus, the same workflow engine can switch between different prompt templates, output field sets, and post-processing rules for different purposes without maintaining a completely independent system for each purpose. For step S130, the content generation module 130 assembles the form parameters (including content purpose, title, primary keywords, secondary keywords, language, region, audience, tone, word count, supplementary information, and frequently asked questions related to the website's purpose) into structured prompts with fixed field order or template placeholders via a prompting engineer. This is then input into a language model based on the Transformer architecture, generating text such as body text, title, description, or frequently asked questions that meet SEO and usage requirements using an autoregressive or sequence-to-sequence approach. The AI ​​content workflow branches based on content purpose, with each branch connecting to an independent language model node. Each node uses system prompts and input templates specific to that purpose and can optionally be equipped with a knowledge base retrieval node to inject retrieval fragments into prompts or post-processing. The output of each branch is aggregated into a variable summary node as the final result of the workflow. The content generation is an asynchronous process: the backend receives the generation request, starts the workflow, and sends back an execution identification code. Upon completion, the result is written to the database, and the frontend queries the status using a polling or push mechanism until completion. For step S140, the design generation module 150 uses a diffusion model to map text prompts to high-resolution images. Before being fed into the diffusion model, the system can first optimize or expand the prompts using a language model to make the visual elements more consistent with the main text theme, brand language, or layout intent, forming a two-stage process; multiple candidate images can be generated for the same prompt at once for the user to choose from or regenerated. The generated images can be written to the media library or the list of generated images for this session (HK 30137923 A Manual 9), and can distinguish between roles such as banner images and body images to facilitate subsequent column mapping.Figure 3 schematically illustrates the process of automatic identification and contractual adaptation for multi-platform publishing, which is one of the key technical aspects of this invention. Referring to Figure 3, in step S510, the multi-platform automated publishing module 170 receives the content and images to be published; in step S520, the platform adapter 171 automatically identifies the content specifications of the target publishing platform. Specifically, platform adapter 171 maintains an adaptation description for each target platform and identifies the platform's content specifications through one or a combination of the following methods: First, when the target platform provides a capability query interface, platform adapter 171 calls the interface to obtain the platform's current character limit, image specifications (size, aspect ratio, format), supported text formats (such as HTML or Markdown), and available metadata fields (such as categories, tags, layouts, article or page types); Second, when the target platform does not provide a capability query interface, platform adapter 171 identifies the platform based on the specifications pre-registered in the corresponding adaptation description; Third, during trial submissions, platform adapter 171 parses the verification results or error messages returned by the platform (such as exceeding character limits, incorrect image sizes, missing fields, or insufficient permissions), and corrects and updates the platform's content specifications accordingly, forming an accumulative identification result. Thus, platform adapter 171 can adapt to pre-set platform requirements and automatically identify and adapt when platform requirements change or new platforms are added. Continuing with reference to Figure 3, in step S530, based on the identified content specifications, the content and accompanying images are format-converted (e.g., HTML to Markdown conversion, cropping or expanding according to word count limits), and the terminology policy is adapted (e.g., replacing or filtering terms according to platform-banned words or compliance requirements), and the semantic mapping of fields is performed (mapping the content's title, body text, summary, URL abbreviation, image replacement text, banner images, and internal images to the corresponding fields, categories, layouts, article types, or page types of the target platform or content management system); in step S540, the data to be published that conforms to the target platform's specifications is obtained; in step S550, the data to be published is submitted to the target platform according to the publishing status (immediate publishing, scheduled publishing, or saved as draft), and logs can be recorded, the publishing status can be confirmed, and completion or failure notifications can be sent. Specifically, when a new platform needs to be integrated, only the corresponding platform adapter needs to be added to the new platform (HK 30137923 A specification 10) (e.g., adding an adaptation description and submission path). The platform adapter 171 can then identify and adapt to the content specifications of the new platform in the above manner, without rewriting the core generation modules such as the content generation module 130 and the design generation module 150, thereby significantly reducing the expansion cost. Figure 5 schematically illustrates the process diagram of adaptive learning closed loop and knowledge base retrieval constraints.Referring to Figure 5, in step S610, the user edits the generated content, including modifications to style, structure, content, and tone; in step S620, the adaptive learning module 160 records the editing behavior and stores it in the user preference library; in step S630, the generation parameter weights are adjusted or the writing model is updated accordingly; in step S640, subsequent generation is made closer to the user's style, forming a continuously evolving personalized closed loop. The knowledge base 140 is invoked during content generation to provide citations, prohibitions, and background constraints through enhanced retrieval methods, reducing the risk of inconsistencies with organizational facts or fabricated details. The knowledge base 140 can integrate internal and external multi-source data, including internal company documents, industry reports, authoritative websites, and historical content, and uses semantic vector retrieval to extract referential fragments related to the current topic as sources for citations, prohibitions, or background constraints. In an exemplary embodiment, the front end provides a reusable step navigator, whose step definition includes step identification, display name, corresponding route, and description. The navigator highlights completed, current, and pending statuses based on the current path or the passed-in current step, and can be set to allow only sequential progression or allow free navigation. For SEO promotion, the steps can be defined as content generation, design generation, and automatic upload. In addition, the system provides a quick creation entry on the entry page: users can select a piece of content from the content list and generate their own images or select multiple images from the media library, then enter the upload form with one click and pre-fill the title, description, URL, body text, and image replacement text; or select the complete process to sequentially execute content generation, design generation, and automatic upload, thereby balancing operational efficiency and process guidance. In an exemplary embodiment, the semantic mapping of the column, in addition to the format conversion of the presentation layer, also maintains a configurable mapping relationship at the levels of category, layout, article or page type, and platform-specific relay data or external columns, so that the content maintains the correct information architecture in the destination system, rather than just presenting text; when the platform returns column verification failure, character limit exceeding, or insufficient permissions, the platform adapter 171 feeds back an actionable prompt message instead of handling it as a silent failure, thereby reducing the maintenance cost of cross-site structure drift and manual site-by-site troubleshooting. In an exemplary embodiment, the multi-platform automated publishing module 170 can collect performance data such as interaction rate, number of shares, and traffic of each platform after publishing, and write the performance data back to the system 100 for the keyword research and grouping module 120 to adjust keyword weight and topic priority, and for the content generation module 130 to adjust subsequent generation parameters, thereby forming a strategy closed loop from output, publishing to feedback together with the adaptive learning module 160. To illustrate the implementation process of each step more completely, the following explanation will be provided in the context of two different application scenarios.In the first application scenario, the selected content is intended for blog posts, falls under the SEO category, and requires images. The user enters seed keywords on the theme planning page, setting the language to Simplified Chinese and the region to Mainland China. Interface 110 retrieves an expanded keyword list. Module 120 groups keywords based on semantics and search intent, generating focus words and 3-4 secondary words, and uses a language model to generate candidate topics. The user selects a topic and saves it as a suggestion. Moving to the content generation step, the user selects the content purpose as a blog post. The system applies preset parameters for this purpose (e.g., language Simplified Chinese, professional tone, approximately 1500 words) from a preset lookup table. Because it falls under website-related purposes, a FAQ section is displayed and included in the request. The user selects a topic from the suggestions, and the system backfills the title, keywords, and description into the form. Upon submission, it triggers a dedicated AI content workflow for this purpose, combining knowledge base 140 to generate the main text, title, description, and FAQs using a search-enhanced generation method. Because images are required, the system moves to the design generation step, where module 150 generates a banner image and body image using a diffusion model. Upon entering the automatic upload step, because the SEO category corresponds to WordPress 181, the platform adapter 171 recognizes the WordPress content specifications (supporting HTML, allowing setting post status, format, post or page type, and category metadata), converts the main text to HTML, and maps the title, summary, URL abbreviation, image alternative text, and category to the corresponding WordPress fields. It publishes and submits the content at scheduled times according to the user's selection, logs the completion, and sends a notification. HK 30137923 A Instruction Manual 12 In the second application scenario, the selected content is for social media posts, belonging to the social media category and requiring images. The keyword research and theme planning process is similar to the first application scenario. In the content generation step, the user selects a social media post, and the system applies preset parameters for that purpose (e.g., lively tone, shorter word count, higher conversational level). Because it is not related to the website, the FAQ section is hidden and not included in the request, thus maintaining the isomorphism of the front-end and back-end field sets; the AI ​​content workflow uses a prompt template specific to social media purposes to generate short text and hashtags. The design generation steps use a diffusion model to generate square or vertical images. In the automatic upload step, the platform adapter 171 identifies the content guidelines of Facebook 182, Instagram 183, and Xiaohongshu 184 (such as character limits, image size and aspect ratio, support for hashtags, and differences in word policies), performs format conversion, word adaptation, and column semantic mapping on the same text and images respectively, and obtains the data to be published that meets the guidelines of each platform. Based on the user's selection, the data is submitted to the aforementioned platforms for immediate publication.As can be seen from these two application scenarios, this invention can automatically complete fine-grained adaptation and publishing for different content uses and target platforms using the same process and generation core. Those skilled in the art will understand that all or part of the steps of the above embodiments can be implemented by computer program instructions and related hardware, and the program can be stored in a computer-readable storage medium. In an exemplary embodiment of this invention, a computer-readable storage medium is also provided, on which a computer program is stored, which, when executed by a processor, implements the above method; and an electronic device includes a processor and a memory for storing processor-executable instructions, the processor executing the above method by executing the executable instructions. In an exemplary embodiment, the solution of this invention can be implemented based on an open-source software library. The front end can use a single-page or multi-page application framework to build the interface, and the back end can use a workflow orchestration engine to connect nodes such as parameter parsing, prompt assembly, model calling, and output post-processing; the functions of content generation, image generation, platform adaptation, and publishing can be implemented by different processors or services, and can be deployed on a server or in the cloud. The above embodiments are merely examples. Without departing from the spirit and scope of the invention, equivalent substitutions or additions can be made to the number of steps, step names, interface paths, form fields, and platform types as described in HK 30137923 A Specification 13. It should be noted that although several modules or units are mentioned in the detailed description above, this division is not mandatory; in fact, according to embodiments of the invention, the features and functions of two or more modules or units described above can be embodied in one module or unit, and conversely, the features and functions of one module or unit described above can be further divided into multiple modules or units. Furthermore, although the steps of the method of the invention are described in a specific order in the drawings, this does not require or imply that these steps must be performed in that specific order, or that all the steps shown must be performed to achieve the desired result. Other embodiments of the invention will readily conceive of those skilled in the art upon considering the specification and practicing the invention disclosed herein. The specification and embodiments are to be considered exemplary only, and the true scope and spirit of the invention are indicated by the claims.HK 30137923 A Claim 1 1. A method for intelligent content generation and automated multi-platform publishing, characterized by comprising: calling a real-time keyword data interface to obtain keyword data, and grouping it according to semantics and search intent to generate focus words and secondary words; applying preset parameters according to the selected content purpose and assembling structured prompts to trigger an AI content workflow to generate content, and generating accompanying images based on the content; automatically identifying the content specifications of the target platform through a platform adapter, the content specifications including word limits, image specifications, format types and metadata fields of the content management system; performing format conversion, word matching and field semantic mapping on the identified content and accompanying images according to the identified content specifications to obtain data to be published that conforms to the specifications of the target platform and submitting it for publication; when a new platform is connected, expanding the corresponding platform adapter to identify and adapt to the content specifications of the new platform. 2. The method according to claim 1, characterized in that, the step of grouping and generating focus words and secondary words according to semantics and search intent includes: grouping keywords according to semantic similarity and search intent, with each group generating one focus word and 3-4 secondary words; the real-time keyword data interface is a search volume query service interface or a keyword planning service interface. 3. The method according to claim 1, characterized in that, it further includes topic planning: generating candidate topics using a language model based on the selected content purpose, language, and region, the candidate topics including titles, keywords, and descriptions, and storing the selected candidate topics as topic suggestions; when generating content, filtering the topic suggestion list according to the selected content purpose, and when selecting a topic suggestion, backfilling the title, keywords, and description of the topic suggestion into the content generation form. 4. According to claim 1, the method is characterized in that the step of applying preset parameters according to the selected content purpose includes: applying preset parameters from a preset value lookup table according to the selected content purpose, wherein the preset parameters include language, region, tone, writing style, word count, and audience; when the selected content purpose is related to the website, a frequently asked questions block is displayed, and the frequently asked questions are included in the request carrier sent to the backend (HK 30137923 A claim 2), so that the set of columns visible on the interface is consistent with the set of columns that can be parsed by the backend. 5. According to claim 1 or 4, the method is characterized in that it further includes dynamically determining the process steps according to the selected content purpose: when the selected content purpose is a type that does not require an image, the process steps are content generation, content checking and adjustment, and preview or upload; otherwise, an image generation step is inserted into the process steps. 6. According to claim 1, the AI ​​content workflow is characterized in that the selected content purpose is used as a condition for branching, each branch adopts a prompt template and processing flow specific to that content purpose, and generates content in a search-enhanced generation method in conjunction with a knowledge base.7. The method according to claim 1, wherein generating the accompanying image based on the content includes: mapping the prompt words to the accompanying image using a diffusion model, and optimizing or expanding the prompt words using a language model before sending them to the diffusion model. 8. The method according to claim 1, wherein submitting for publication includes: submitting in the form of immediate publication, scheduled publication, or saving as a draft; setting the article status, format, article or page type, and category for the content management system; and determining the available publishing platform according to the category. 9. The method according to claim 1, further comprising adaptive learning: recording user editing behavior on the generated content, the editing behavior including modifications to style, structure, content, and tone; storing the editing behavior in a user preference library; and adjusting the generation parameters or model weights accordingly. 10. A smart content generation and multi-platform automated publishing system, characterized by comprising: a keyword research and grouping module, used to call a real-time keyword data interface to obtain keyword data, and group it according to semantics and search intent to generate focus words and secondary words; a content generation module, used to apply preset parameters and assemble structured prompts according to the selected content purpose, triggering an AI content workflow to generate content; a design generation module, used to generate accompanying images based on the content; and a multi-platform automated publishing module, which is equipped with a platform adapter, the platform adapter being used to automatically identify the content specifications of the target platform, the content specifications including word limits, image specifications, format types and metadata fields of the content management system, and to perform format conversion, word adaptation and field semantic mapping of the content and accompanying images according to the identified content specifications, to obtain data to be published that conforms to the specifications of the target platform and submit it for publication; and when a new platform is added, the corresponding platform adapter is expanded to identify and adapt to the content specifications of the new platform. HK 30137923 A Instruction Manual Appendix 1 Figure 1 HK 30137923 A Instruction Manual Appendix 2 Figure 2 HK 30137923 A Instruction Manual Appendix 3 Figure 3 HK 30137923 A Instruction Manual Appendix 4 Figure 4 HK 30137923 A Instruction Manual Appendix 5 Figure 5 HK 30137923 A.

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