Information processing method, apparatus, device, and medium

Abstract

This application discloses an information processing method, apparatus, device, and medium, applied in the field of Internet technology. The method includes: displaying a product information browsing page for a target product; displaying demand analysis information related to the target product's needs on the product information browsing page; the demand analysis information includes at least one scene feature, at least one account feature, and at least one product feature related to the target product; responding to a content generation operation triggered on the product information browsing page, displaying at least one generation method related to the demand analysis information on a published content editing page; when each generation method is associated with at least one scene feature and at least one account feature, each generation method is used to intelligently generate published content related to the scene feature and the account feature. Using this application embodiment can improve the content quality and publishing effect of published product content.

Description

Technical Field

[0001] This application relates to the field of Internet technology, specifically to the field of data display technology, and in particular to an information processing method, apparatus, device, and medium. Background Technology

[0002] Currently, in order to improve the conversion rate of users to purchase products, merchants choose to publish product notes. For example, merchants can edit the content text around the relevant information of the product (such as the appearance of the product, the effect of use, etc.) and publish notes with the product attached. When users view the content text, they can enter the product details page through the attached product link to make a purchase.

[0003] Therefore, for product notes, the quality of the text describing the product and its appeal to users will affect the product's transaction conversion rate. If the merchant lacks experience or has low creative ability, not only will the content creation efficiency be low, but it will also be difficult to edit and publish high-quality content, resulting in low publishing effect of product notes. Summary of the Invention

[0004] This application provides an information processing method, apparatus, device, and medium that can improve the content quality and publishing effect of product publishing content, thereby increasing product conversion rate through product publishing content.

[0005] On one hand, embodiments of this application provide an information processing method, which includes: The product information browsing page of the target product is displayed. On the product information browsing page, demand analysis information related to the target product demand is displayed. The demand analysis information is determined based on the published content related to the target product. The demand analysis information includes at least one scenario feature, at least one account feature and at least one product feature related to the target product. In response to a content generation operation triggered on the product information browsing page, at least one generation method related to the demand analysis information is displayed on the published content editing page; when each generation method is associated with at least one scene feature and at least one account feature, each generation method is used to intelligently generate published content related to the scene feature and the account feature.

[0006] On one hand, embodiments of this application provide an information processing method, which includes: In response to the content generation operation performed on the target product, the published content editing page is displayed; the published content editing page includes an intelligent generation entry point; In response to a trigger operation targeting the intelligent generation entry, at least one generation method is displayed; wherein each generation method is associated with at least one scene feature and at least one account feature; wherein the scene feature and account feature are both determined based on the published content related to the target product; In response to the selection operation of the target generation method among at least one generation method, the target publishing content of the target product is generated based on the target scene features and target account features associated with the target generation method.

[0007] On one hand, embodiments of this application provide an information processing apparatus, which includes: The information display module is used to display the product information browsing page of the target product. The product information browsing page displays demand analysis information related to the target product demand. The demand analysis information is determined based on the published content related to the target product. The demand analysis information includes at least one scenario feature, at least one account feature, and at least one product feature related to the target product. The page display module is used to respond to the content generation operation triggered on the product information browsing page and display at least one generation method related to the demand analysis information on the published content editing page; when each generation method is associated with at least one scene feature and at least one account feature, each generation method is used to intelligently generate published content related to the scene feature and the account feature.

[0008] On one hand, embodiments of this application provide an information processing apparatus, which includes: The editing page display module is used to display the published content editing page in response to the content generation operation performed on the target product; the published content editing page includes an intelligent generation entry point; The editing page display module is also used to respond to trigger operations on the intelligent generation entry and display at least one generation method; wherein, the scene features and account features are both determined based on the published content related to the target product; The content generation module is used to respond to the selection operation of the target generation method among at least one generation method, and generate the target publication content of the target product based on the target scene features and target account features associated with the target generation method.

[0009] On one hand, embodiments of this application provide an electronic device including a processor and a memory, wherein the memory is used to store a computer program, the computer program including program instructions, and the processor is configured to invoke the program instructions to execute some or all of the steps in the above method.

[0010] On one hand, embodiments of this application provide a computer-readable storage medium storing a computer program, the computer program including program instructions, which, when executed by a processor, are used to perform some or all of the steps in the above-described method.

[0011] Accordingly, according to one aspect of this application, a computer program product or computer program is provided, which includes computer instructions that, when executed by a processor, can implement some or all of the steps in the above-described method.

[0012] In this embodiment, demand analysis information related to the target product demand can be displayed on the product information browsing page. This target product demand characterizes the purchase demand for the target product, such as the needs of users when purchasing or learning about the target product. The demand analysis information is determined based on the published content related to the target product. For example, the demand analysis information can include at least one scenario feature, at least one account feature, and at least one product feature related to the target product. For instance, it can be some scenario features (i.e., some applicable scenarios for the target product) determined through the published content of the target product, or some account features (such as the group of users interested in the target product demand when purchasing it) determined through the target product demand, or some product features related to the target product demand determined around the target product. That is, some product characteristics related to the target product can be determined through online content analysis, thereby enabling a response to… The content generation operation triggered on the product information browsing page displays at least one generation method related to the demand analysis information on the published content editing page. This means that several generation methods can be derived from one or more combinations of the demand analysis information. For example, each generation method can be associated with at least one scene feature and at least one account feature. Therefore, published content related to the scene and account features can be intelligently generated based on the generation method. For instance, based on the generation method, content text related to the target product can be generated more conveniently around the scene and account features. This not only improves the efficiency and convenience of content generation but also allows for higher-quality content generated based on the product characteristics analyzed from the target product's information. This content is more closely aligned with the target product (e.g., incorporating descriptions that appeal to a specific group or combining product features that attract users under specific purchasing needs). This enhances the attractiveness of the published content to users, thereby improving the effectiveness of product notes and product conversion rates. Attached Figure Description

[0013] To more clearly illustrate the technical solutions of the embodiments of this application, the drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0014] Figure 1 A schematic diagram of a network architecture provided for an embodiment of this application; Figures 2a-2b A schematic diagram illustrating an information processing scenario provided in an embodiment of this application; Figure 3 A flowchart illustrating an information processing method provided in this application embodiment. Figure 1 ; Figures 4a-4c This application provides a schematic diagram of a product information browsing scenario. Figure 5 A flowchart illustrating an information processing method provided in an embodiment of this application is shown below; Figures 6a-6b This is a schematic diagram illustrating a configuration scenario for a generation method provided in an embodiment of this application; Figure 7 A flowchart illustrating an information processing method provided in this application embodiment. Figure 3 ; Figures 8a-8b This application provides a schematic diagram of a content generation scenario. Figure 9 A flowchart illustrating an information processing method provided in this application is shown in Figure 4. Figures 10-11 This is a schematic diagram of the structure of an information processing device provided in an embodiment of this application; Figure 12 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. Detailed Implementation

[0015] The information processing method proposed in this application is implemented in an electronic device, which can be a server or a terminal. The server can be a standalone physical server, a server cluster or distributed system composed of multiple physical servers, or a cloud server providing basic cloud computing services such as cloud services, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, and big data and artificial intelligence platforms. The terminal can be a smartphone, tablet computer, laptop computer, desktop computer, etc., but is not limited to these.

[0016] In the description of the embodiments of this application, the published content may refer to notes (including text and images), short videos, medium-length videos, etc., published by users on content platforms. Alternatively, it may also include live streams, instant (a type of short content that is shared and exists in real time), evaluations of products or any type of interest (such as locations, music, etc.) or media data (such as movies, music), comments in notes, bullet comments, topic discussions, interest points (such as group chats, live streams, etc.), routes (such as cycling routes, travel guides, etc.). The specific content information included in the published content applicable to different scenarios can be adjusted accordingly, and the content information in the published content can be determined by the publisher (such as the target audience). The specific type of published content is not limited here. The published content will be stored on the server and distributed to users browsing the content platform through a content distribution algorithm deployed on the server (for example, after a user clicks on a published piece of content on the platform's homepage, the next time the page refreshes, other published content published by the account that published the clicked published content can be distributed to that user).

[0017] The target products involved refer to the purchasable products (product A, product B) listed by the merchant. The target product category refers to the category to which the target product belongs. A product category can be associated with multiple products (for example, if the product category is "down jacket", there are multiple products with "down jacket" under this product category, such as product A being "XX down jacket", product B being "YY down jacket", etc.).

[0018] Among them, the target product demand is the user's purchase demand for this type of product under the target product category (for example, if the product category is "down jacket", the product demand can be "warmth", which means that when users want to buy "down jacket", their demand and focus is on warmth, or the product demand can be "durable fabric", which means that when users want to buy "down jacket", their demand and focus is on durable fabric).

[0019] Therefore, under a target product demand, there are one or more demand analysis information. For example, scenario characteristics, account characteristics, and product characteristics. Among them, product characteristics refer to the objective product parameters (i.e., product selling points) determined around the target product under the target product demand. For example, if the product category is "down jacket", the product demand could be "warmth", and the product selling points could be "down filling volume" or "cold resistance certification", etc.

[0020] Account characteristics refer to the group profile of users with demand for the target product, such as "urban commuters" or "outdoor sports enthusiasts." Scenario characteristics refer to the usage scenarios of the target product given the demand, such as "extreme cold weather travel scenarios" or "family life scenarios."

[0021] Specifically, at least one generation method can be obtained by combining one or more information from scenario features, account features, and product features to generate content for the target product. This content is then used to generate published content for the target product. In other words, when publishing content that meets the needs of the target product is generated using at least one method, users can quickly learn about the product's usage scenarios, target audience profiles, or relevant selling points from the published content. This attracts users with a need for the target product to learn about or purchase it. Therefore, it not only improves the content quality and relevance of the published content to the target product but also effectively conveys relevant product information. Furthermore, by creating content data based on product demand, it accurately attracts users to purchase the product, thereby increasing the conversion rate.

[0022] The term "in response to" indicates a state where a corresponding event occurs or a condition is met. The timing of subsequent actions performed in response to this event or condition is not necessarily strongly correlated with the time when the event occurs or the condition is met. For example, "in response to XX operation, execute the target step" means that the target step is triggered upon detection of "XX operation".

[0023] For example, in some cases, subsequent actions can be performed immediately when the event occurs or the condition is met; while in other cases, subsequent actions can be performed some time after the event occurs or the condition is met, meaning there can be an intermediate process between responding to the corresponding event or condition and the subsequent actions performed.

[0024] The term "trigger operation" refers to an action performed on certain information (such as a control). This operation sends a corresponding instruction to the electronic device, triggering it to execute the next task. The next task triggered by different trigger operations on different information can all be pre-set. This trigger operation can be a contact-based operation such as clicking, double-clicking, long-pressing, or swiping, or other preset event operations, or it can be a non-contact operation (such as voice instructions). In some cases, the trigger operation can also be executed by the electronic device based on a pre-defined program. No further limitations are specified here.

[0025] One network architecture diagram proposed by the information processing method can be shown as follows: Figure 1As shown, the network architecture may include server 100 (the number of servers is not limited) and a cluster of terminal devices (the number of terminal devices is not limited, such as terminal device 200a, terminal device 200b, ..., terminal device 200n), wherein communication connections may exist between servers. Simultaneously, a server may have a communication connection with any terminal device, so that the server can interact with the terminal device through this communication connection. The aforementioned communication connection is not limited in method; it can be a direct or indirect connection via wired communication, a direct or indirect connection via wireless communication, or other methods, which are not limited herein. Furthermore, it is understood that the electronic devices involved in the embodiments of this application may be... Figure 1 The terminal device shown can also be Figure 1 The server shown.

[0026] It should be understood that, such as Figure 1 Each terminal device in the terminal device cluster shown can be equipped with an application client for information processing. This application client can be of any type, such as a social networking client, instant messaging client (e.g., a conferencing client), entertainment client (e.g., a live streaming client), multimedia client (e.g., a video client), information client (e.g., a news client), shopping client, or any other client capable of displaying text, images, audio, and video data. No specific type of application client is limited here.

[0027] For example, an application client refers to a client that can send and receive internet messages instantly and has information functions. Specifically, terminal devices (such as…) Figure 1 The terminal device 200a) shown in the diagram has an application client that displays a product information browsing page related to the target account. When a user selects to view a target product on the product information browsing page, a request to view the target product is sent to the server. The server obtains the demand parsing information for the target product and returns it to the terminal device for display. The demand parsing information can be determined by the server in real time or predetermined (e.g., demand parsing information for relevant product categories is determined daily).

[0028] Select at least two routes on the map page to instruct the server to generate a target route based on these two routes, which will then be displayed on the map page by the application client.

[0029] Optionally, the aforementioned terminal devices and servers can be logically separated. Therefore, when referring to terminal devices and servers below, they may be physically the same device or different devices.

[0030] For further information, please refer to [link / reference]. Figures 2a-2b , Figures 2a-2b This is a schematic diagram illustrating an information processing scenario provided in an embodiment of this application. It shows a product information browsing page for the target product (e.g., ...). Figure 2a The target product can be a product listed by the target account, which can be a merchant account. For example, a merchant account can manage products (such as listing and restocking) and orders (such as shipping) on ​​the product information browsing page. In addition, it can also view product requirements. For example, it can select a product category on the product information browsing page (such as directly selecting a product category, or when selecting a product in the product library under the merchant account, specifying the product category of that product) to view at least one product requirement related to that product category.

[0031] When a target product requirement is selected from at least one product requirement, requirement analysis information related to that target product requirement can be displayed. For example, the requirement analysis information may include at least one scenario feature, at least one account feature, and at least one product feature related to the target product.

[0032] For example, a product information browsing page can display descriptions of the target product's demand, multiple group profiles related to that demand (such as profiles of highly popular groups), selling points of related products, and usage scenarios for products within the target product category (such as the target product). These usage scenarios are determined by content tags based on relevant content published within the target product category (such as content associated with the target product). Therefore, it can display some published content as a reference for content creation, along with the content tags determined by that published content (the tag information of the content tags constitutes the scenario characteristics).

[0033] The demand analysis information is determined based on the published content related to the target product. Optionally, the demand analysis information can also be further determined based on search history and transaction history. For example, account characteristics can be determined by at least one of search history, transaction history, and published content, while scenario characteristics and product characteristics can be determined by the published content.

[0034] In this way, based on the relevant online content, we can determine the product demand of users for a certain product (i.e., a certain product category), so as to understand the user audience for the specific product or the information that different users want to know. Based on this product demand, we can create relevant high-quality content to attract users to buy the product.

[0035] Therefore, by triggering a content generation operation on the product information browsing page, the published content editing page (such as...) can be displayed. Figure 2bThe content editing page can display at least one generation method related to the demand analysis information, such as a generation method derived from scene features and account features (i.e., scene x audience). In this case, the generation method can automatically associate with the product features under the target product demand. Alternatively, the generation method can directly display scene features x account features x product features.

[0036] The generation method can be used to intelligently generate content related to scene and account characteristics. For example, a user can select a target generation method to create content according to that method and obtain relevant content data.

[0037] In specific embodiments of this application, scenarios involving the acquisition of user information and related data, such as the acquisition of user information (e.g., published content), require user permission or consent. That is, when these embodiments are applied to specific products or technologies, the collection, use, and processing of relevant user data comply with the relevant laws, regulations, and standards of the relevant regions. For example, interactive pages can be used to provide prompts indicating which data will be collected or acquired. Specifically, lists or other methods can be used to present the types and content of this data to the user. Data collection and processing will only proceed after a confirmation or instruction to allow data collection is received on the interactive page.

[0038] The scenarios described above are merely examples and do not constitute a limitation on the application scenarios of the technical solutions provided in this application. The technical solutions of this application can also be applied to other scenarios. For example, as those skilled in the art will know, with the evolution of system architecture and the emergence of new business scenarios, the technical solutions provided in this application are also applicable to similar technical problems.

[0039] Based on the above description, this application proposes an information processing method, which can be executed by the aforementioned electronic device, specifically... Figure 1 The terminal device shown. Please refer to [link / reference]. Figure 3 , Figure 3 A flowchart illustrating an information processing method provided in this application embodiment. Figure 1 .like Figure 3 As shown, the flow of the information processing method in this application embodiment may include the following: S101. Display the product information browsing page for the target product, and display the demand analysis information related to the target product demand on the product information browsing page.

[0040] Among them, the target product refers to the product listed by the target account. When publishing content related to the target product, the target account can preview the product demand for this type of product through the product information browsing page in advance, so as to create content that better meets user needs.

[0041] The target account can refer to a merchant account. The product information browsing page can be used by the target account to perform related product operations or view related products, such as managing products or orders.

[0042] The product information browsing page can be used to view at least one product requirement related to the target product and the requirement analysis information of the product requirement. The generation method is obtained by combining the requirement analysis information, and the generation method can be used to generate content data of the published content related to the target product.

[0043] In this context, product demand is defined specifically for the target product category, representing the purchase demand for products within that category. The target product refers to a specific, purchasable item, and the target product category represents multiple products within a single category. For example, a product account's product library can list multiple down jacket items (such as different styles), and all of these down jacket items fall under the category of "down jackets."

[0044] Therefore, you can view the product demand related to a specific product category on the product information browsing page, which means you can express the product demand for related products under that specific product category.

[0045] Therefore, in response to a category selection operation for a target product category on a product information browsing page, the page can filter and display at least one product requirement related to the target product category. That is, a specific target product category can be selected to view the corresponding product requirements. Since at least one product requirement includes the target product requirement, in response to a selection operation for the target product requirement, requirement parsing information related to the target product requirement can be displayed; that is, when a specific target product requirement is selected, detailed requirement parsing information can be viewed.

[0046] Alternatively, it could be in response to a category selection operation for a target product on a product information browsing page, determining the target product category for the target product, and then filtering and displaying at least one product requirement related to the target product category on the product information browsing page.

[0047] For example, a target account can directly select any product from the product library (such as "XX down jacket"). In this case, the product category can be determined as down jacket, and at least one requirement and requirement parsing information can be obtained and displayed. For instance, when viewing product requirements on a product information browsing page and selecting a specific product, a request can be sent to the server to determine the product category of that specific product.

[0048] Among them, you can enter the content editing page through the product information browsing page to generate and publish content related to the target product (for example, the target product can be selected by yourself on the content editing page, or the target product can be automatically attached when you enter the content editing page after selecting the target product on the product information browsing page).

[0049] Optionally, users can also access the product information browsing page through the content editing page. For example, the product information browsing page can also be displayed by showing the content editing page. When a target product is attached to the content editing page, the target product category is determined, and demand guidance information for the target product category is displayed. The demand guidance information is used to view at least one product demand related to the target product category that contains the target product demand. In response to the triggering operation of the demand guidance information, the product information browsing page containing at least one product demand is displayed.

[0050] For example, when a target account wants to publish relevant content, it can enter the content editing page and attach the target product. When the target product is detected, the target product category can be determined, and a demand guidance prompt can be displayed to guide the target account to the product information browsing page to view the product demand for that target product category.

[0051] Optionally, content generation can also be performed on the product information browsing page. When returning to the published content editing page, at least one generation method related to the requirement parsing information of the specified target product requirement can be displayed on the published content editing page. This allows the viewer to see the generation method obtained by combining the requirement parsing information of the viewed product requirement when entering the published content editing page after viewing a product requirement on the product information browsing page. This enables the target published content related to the target product to be quickly generated through the specified target generation method.

[0052] The demand analysis information is determined based on the published content related to the target product. Furthermore, it can also be determined based on at least one of the following: search records, transaction records, and published content related to the target product. The demand analysis information includes at least one scenario feature, at least one account feature, and at least one product feature related to the target product.

[0053] Taking a target product category as an example, determining product demand can involve obtaining relevant content published about the target product and using the content data within that content to identify the demand. This relevant content refers to content published about the target product category. Such content includes at least one of the following: content containing keywords related to the target product, or content featuring any product within the target product category (e.g., content featuring the target product itself).

[0054] This includes content that includes keywords related to the target product, which in turn includes content that includes keywords related to the target product category. These keywords can be category terms for the target product category (e.g., "down jacket") or category terms similar to the target product category (e.g., "cotton-padded jacket").

[0055] Therefore, the content published related to the target product category can include both product-related content and general content, without limitation. In this case, not only content data (such as the body text and title) can be obtained, but also interaction data (such as user comments) can be acquired.

[0056] This involves acquiring published content from content platforms and clustering it according to product categories to obtain published content related to multiple product categories. Taking a target product category as an example, a target model can be used to extract at least one product requirement for that target product category from the published content related to that category. For instance, a target model (such as a large language model) can be used to analyze the content data and interaction data of the published content related to the target product category to obtain at least one product requirement and the requirement description information for each product requirement.

[0057] At this point, demand analysis information can be determined based on the target product demand. For example, target content related to the target product demand can be obtained from the published content related to the target product category (i.e., the published content related to the target product includes target content related to the target product demand). For example, keyword matching can be used to determine published content containing demand words related to the target product demand; or semantic similarity can be used to determine published content similar to demand words (or demand analysis information) related to the target product demand.

[0058] Among them, at least one scenario feature is extracted based on the scenario description in the target published content; at least one account feature is extracted based on the account description in the target published content, the search account corresponding to the search record, and the transaction account corresponding to the transaction record; and at least one product feature refers to the product feature related to the target product demand extracted based on the product description in the target published content.

[0059] At this point, further demand analysis information can be determined from the target content published related to the demand for the target product. This can be achieved by analyzing the content data and interaction data of the target content published related to the demand for the target product using a target model (such as a large language model). For example, a scenario description can be obtained from the content data and interaction data of the target content published related to the demand for the target product using a target model, and at least one scenario feature can be extracted based on the scenario description. This scenario feature can be considered as a content tag for the published content. The scenario description refers to descriptions related to the usage scenarios of the target product category.

[0060] For example, account descriptions can be obtained from content data and interaction data of target product demand-related published content through a target model. At least one account feature can then be extracted based on the account description. This account feature can be viewed as a group profile of the target product category's target audience, determined through the published content. The account description, in this context, refers to descriptions related to the target product category's user group.

[0061] For example, a product description can be obtained from the content data and interaction data of the target published content related to the target product demand through a target model. Based on the product description, at least one product feature related to the target product demand can be extracted, that is, a product feature that meets the target product demand. This product feature can be regarded as a relevant product parameter (i.e., product attribute) of the target product category determined by the published content. Here, the product description is an objective description of the target product category itself.

[0062] For example, the content data for the target post could be: "Blueberries are so convenient! Just open the bag and eat, no washing or cutting required, perfect for busy working people like me. They're also rich in anthocyanins, good for the eyes, ideal for people who spend long hours in front of a computer." The resulting product demand could be "ready to eat straight from the bag." The scenario description could be "perfect for busy working people like me," thus identifying the scenario characteristic "breakfast scenario." Similarly, the account description could be "perfect for busy working people like me," thus identifying the account characteristic "working person." And the product description could be "Blueberries are so convenient," thus identifying the product characteristic as "easy to eat," etc.

[0063] Optionally, in addition to obtaining information through published content, more information can be added. For example, at least one account feature can be extracted based on the account description in the target published content, the search account corresponding to the search history, and the transaction account corresponding to the transaction history.

[0064] For example, it can obtain the search account corresponding to the search records related to the target product (i.e., search records of searching for keywords related to the target product category), and it can obtain the transaction account corresponding to the transaction records related to the target product (i.e., transaction records of trading any product under the target product category).

[0065] At this point, search accounts and transaction accounts can be categorized, and the resulting account characteristics (i.e., group profiles) can be used as supplementary features to at least one account characteristic for the target product demand. For example, account tags can be extracted from search and transaction accounts, such as those extracted based on published content or account data, like age, gender, and identity verification. Search and transaction accounts can then be categorized based on these tags, with each category corresponding to a group profile, or frequently occurring account tags can be used as a group profile. In this case, the account characteristics obtained from search and transaction accounts can be considered universal account characteristics for all product demands; that is, these account characteristics can be included under each product demand within the target product category.

[0066] Alternatively, account characteristics can be interest tags, such as interest tags compiled for relevant accounts (e.g., accounts that publish content related to the target product demand, search accounts, and transaction accounts). For example, interest tags (i.e., the account's hobbies on the content platform, such as frequently following fitness or cooking) can be determined based on the account data (e.g., account verification, account profile) and interaction data (e.g., published content, comments, watched live streams, purchased products). In this case, interest tags can be used to determine account tags for the target product demand.

[0067] For example, these interest tags can be used as account features, or frequently occurring interest tags can be used as account features (in this case, they can be considered as general account features for all product needs). Alternatively, interest tags related to the target product need can be obtained and used as account features, or frequently occurring interest tags can be used as account features. For example, if the target product need is "weight loss and low sugar," and the interest tag is "frequently following weight loss tutorials," then it can be determined that the target product need is related to this interest tag. The relevance can be determined based on the domain of the target product need and the interest tag, or based on semantic similarity, or even by a large model's intelligent judgment; no specific limitations are imposed here.

[0068] For example, one can obtain product description data (such as user manuals, related tutorials, product information, etc.) provided by the merchant, and extract product features related to the target product requirements from the product description data as a feature supplement to at least one product feature of the target product requirements.

[0069] It is understandable that the extracted product demand information (scenario features, account features, and product features) consists of frequently occurring high-frequency features. For example, a large number of posts related to the target product demand have the scenario feature of "low-fat afternoon tea," and a large number of posts have the account feature of "office workers." This reflects what information users typically want to know or are interested in under the target product demand. Consequently, some posts that match these features can be generated to make the content more refined and specific. This not only lowers the barrier to content creation for merchants and improves the ease of content creation, but also improves the efficiency of generating content based on demand scenarios, increases the matching degree and attractiveness of the posted content with specific user needs, and thus promotes the efficiency of product seeding and conversion rates.

[0070] Optionally, at least one product demand suitable for the target product can be identified from the published content related to the target product category. Furthermore, one or more product characteristics that users who are interested in purchasing or buying the target product can be obtained from the target published content related to the target product demand. Based on these product characteristics, some generation methods can be derived to generate target product content for the target product, thereby attracting more users who are interested in the target product demand to view or purchase the target product.

[0071] Optionally, in addition to content generation for published content, it can also be applied to live streaming script generation in live streaming scenarios. In this case, the live streaming script can be generated directly based on the demand analysis information of the product demand obtained from the published content. Alternatively, it can be generated by obtaining live streaming content related to the target product (such as live streaming content that includes any product under the target product category, which may include the host's script, user comments, etc. when live streaming any product under the target product category), and determining the product demand and demand analysis information in the above manner, thereby generating the live streaming script through the obtained generation method.

[0072] Furthermore, it can be applied to content recommendation scenarios. For example, when generating content data for a target content to be published based on scene and account characteristics, and then publishing that target content, the target content can be recommended to users who match those account characteristics. For instance, if the account characteristic is "office worker," then the target content can be recommended to users whose account is tagged as "office worker."

[0073] For example, targeted content can be recommended to users with a need for the target product. If a user is detected to have searched for terms related to the target product (such as "ready to eat"), then targeted content generated based on that "ready to eat" product need can be recommended to such users. No specific application scenario is limited here.

[0074] Optionally, the product information browsing page displays account feature information blocks. One account feature information block corresponds to one account feature, meaning that one account feature information block can be used to display relevant content for a group profile.

[0075] At this time, in response to the triggering operation of the account feature information block, the account details viewing page of the target account feature corresponding to the triggered account feature information block can be displayed, and the feature details information of the target account feature and / or the first published content related to the target account feature in the published content related to the target product can be displayed on the account details viewing page.

[0076] For example, on the account details page, you can view detailed information about the target account's characteristics, such as a specific description of the target user profile. You can also display the first published content related to the target account's characteristics—that is, posts containing account descriptions relevant to those characteristics. For instance, the first published content can be determined from the target's posts based on keyword matching or semantic similarity matching.

[0077] At this point, the published content can be taken to the content details page. This page highlights the account description related to the target account's characteristics, so that the target account can understand how to describe the target group when creating content for the target product.

[0078] Optionally, the product information browsing page can also display some creative references, i.e., high-quality published content for the target account to view. For example, the scene characteristics are determined from the target published content that meets the needs of the target product, so the published content related to the target product includes multiple secondary published content associated with each scene characteristic.

[0079] At this time, at least one second published content associated with at least one scene feature can be displayed on the product information browsing page, and scene tags indicating the corresponding associated scene feature can be displayed on each second published content.

[0080] For example, high-quality content can be obtained from the second published content associated with a scene feature (such as content with a high number of likes, or content quality can be determined by a content quality processing model based on the content data and interaction data of the published content) as a creative reference and displayed on the product information browsing page.

[0081] It's understandable that when displaying content cards for second-published content on a product information browsing page, the scene features associated with that second-published content can be displayed as scene tags. This second-published content is essentially the published content containing scene descriptions related to those scene features. For example, the second-published content can be determined from the target published content based on keyword matching or semantic similarity matching.

[0082] Optionally, in response to a trigger operation on a scene tag for any of the second published content, the scene details page for the target scene feature corresponding to the triggered scene tag can be displayed; multiple second published content items associated with the target scene feature can be displayed on the scene details page.

[0083] For example, the scene details page can display all the second published content associated with the target scene feature. At this time, the published content can be triggered to enter the content details page. On the content details page, the scene descriptions related to the target account feature can be highlighted so that the target account can understand how to describe specific usage scenarios when creating published content for the target product.

[0084] Optionally, when determining product demand, the above method can be based on obtaining relevant published content within the target time period, such as relevant published content, transaction records, and account records within a week, to determine at least one product demand for the target product category within that week. Furthermore, the demand volume for this product can be determined, for example, based on search records and relevant published content. This could be determined by the number of target published content and the number of interactions (e.g., the sum of the number of target published content, likes, comments, and favorites), or by including search terms related to the target product category, and then including search terms related to the target product demand itself. For example, the search records might contain search terms that simultaneously include keywords related to the target product category and keywords related to the target product demand.

[0085] S102. In response to a content generation operation triggered on the product information browsing page, display at least one generation method related to the requirement analysis information on the published content editing page.

[0086] This can be achieved by guiding users to post content related to their target product needs on the product information browsing page. For example, a content generation guide control can be displayed on the product information browsing page. Triggering this guide control can bring up the content editing page, where the target account can edit content data and attach the target product.

[0087] The content editing page can also display at least one generation method obtained by combining one or more pieces of information from the requirements analysis information.

[0088] For example, each generation method is derived from a combination of at least one scene feature and at least one account feature. When each generation method is associated with at least one scene feature and at least one account feature, each generation method is used to intelligently generate published content related to the scene feature and the account feature.

[0089] In addition, each generation method can be associated with at least one product feature. In this case, each generation method is used to intelligently generate published content related to scene features, account features, and at least one product feature.

[0090] For example, any scene feature and any account feature can be randomly combined to obtain multiple generation methods. For instance, the popularity of each scene feature and each account feature can be obtained, and combinations can be made based on popularity. For example, a specified number of scene features with the highest popularity (e.g., the top 3 scene features, whose popularity can be determined by the number of associated published content) and a specified number of account features with the highest popularity (e.g., the top 3 account features, whose popularity can be determined by the number of associated published content) can be randomly combined to obtain at least one generation method. Each generation method can automatically associate one or more product features.

[0091] This generation method can be determined based on the target product requirements viewed on the product information browsing page when the target account's terminal device requests page data for the content editing page from the server.

[0092] Specifically, in response to the selection of a target generation method among at least one generation method on the published content editing page, the content data generated for the target product based on the target generation method can be displayed on the published content editing page.

[0093] The content data includes one or more of the following: scene descriptions related to the scene features corresponding to the target generation method; account descriptions related to the account features corresponding to the target generation method; and product descriptions related to the product features associated with the target generation method. For example, the target model can generate relevant content data for the target product based on the target generation method. This content data can be used to characterize the target product's usage scenarios, applicable groups, and product attributes, thereby enriching the information covered by the content data and attracting users to view the target product.

[0094] Therefore, in response to content publishing operations on the content editing page, targeted content related to the target product can be published based on content data. This targeted content includes the content data from the content editing page and can include a link to the target product for easy viewing or trading.

[0095] For example, such as Figures 4a-4c , Figures 4a-4c This application provides a schematic diagram of a product information browsing scenario; wherein, in Figure 4a In this system, the product information browsing page not only allows direct filtering of different product categories, but also filtering of specific products. For example, one can view the products in the target account's product library through the product selection entry. Selecting a target product will display at least one product requirement from the target product category on the product information browsing page.

[0096] The product information browsing page can display account feature information block 40a corresponding to the account features. When triggered, it can enter the account details viewing page to view the first published content related to the account features.

[0097] The product information browsing page can also display creative references, such as content cards 40b associated with at least one scene feature and at least one second published content. Triggering a content card leads to the content details page. Furthermore, triggering a content update control allows users to view more published content.

[0098] The content card of the second published content can be associated with scene tags 40c. In addition, triggering any scene tag can enter the scene details viewing page, where multiple second published contents associated with that scene feature can be viewed.

[0099] The product information browsing page can display a content generation guide control 40d. When the content generation guide control is triggered, the user enters the published content editing page. The published content editing page can automatically mount the target product selected on the product information browsing page, or the target product can be manually mounted by the target account.

[0100] In addition, the product information browsing page can also display the generation method obtained by combining the scene features and account features in the demand analysis information displayed on the product information browsing page.

[0101] Among them, Figure 4bIn addition, when responding to a content generation operation performed on a target product and displaying the content editing page, the target product can be attached to the content editing page, the target product category can be determined, and demand guidance information 41a for the target product category can be displayed. This demand guidance information is used to view at least one product demand related to the target product category, and this at least one product demand includes the target product demand. In other words, it can guide users to view relevant product demands to determine how to generate the content for the target product.

[0102] This can trigger a demand guidance prompt, displaying a product information browsing page containing at least one product demand, allowing users to view the demand parsing information for the target product demand. The product information browsing page can either automatically select the target product category or select the target product itself.

[0103] Among them, Figure 4c In this application, the purpose is to describe products from the perspective of user needs in order to obtain relevant content data (such as titles, body text, etc.), thereby providing an information basis for matching supply and demand for users.

[0104] This application requires identifying the product demand for the product category to which the target product belongs, and determining the demand description information, scenario characteristics (usage scenarios), account characteristics (group profile), and product characteristics (product selling points) around this product demand. Specifically, the product demand description information is a detailed description of the product demand; the scenario characteristics are the usage scenarios of the target product under the product demand; the account characteristics are the applicable group for the target product under the product demand; and the product characteristics are some objective selling points of the target product under the product demand. Based on this demand analysis information, content that matches a specific product demand can be generated. When users view the published content, it can stimulate their potential product demand, thereby attracting users to purchase the product.

[0105] The application of product demand can include memory-based content creation, live stream script creation, and content recommendation. The data acquired may differ depending on the specific application scenario.

[0106] For example, in the context of content creation for publishing, one can obtain relevant product content used to determine product demand (such as relevant product content generated within a certain time period), as well as product publishing content and general publishing content (which may include images, titles, body text, comments, etc.). Similarly, in the context of live stream script creation, one can obtain live stream content (such as live stream explanations, live stream comments, etc.). Furthermore, search history and transaction records can also be obtained.

[0107] Upon acquiring relevant product content, data processing can be performed. Taking in-memory content creation as an example, relevant product content can be categorized according to product type, such as acquiring published content, transaction records, and search records related to the target product category. Furthermore, deduplication and noise filtering can be performed on the relevant product content of the target product category (e.g., removing low-quality published content, deduplicating similar published content, etc.). Subsequently, the target model can be used to parse at least one product requirement and the requirement parsing information for each product requirement based on the relevant product content of the target product category.

[0108] This includes evaluating the model results of the target model, which can be done manually or through multi-model evaluation. For example, the best large model can be obtained by evaluating the output results of multiple large models and then using it as the target model.

[0109] Therefore, for merchants, when publishing product content to attract users, the only information they can refer to is the objective content of the product itself, product reviews, or related content published by other users. This requires merchants to not only view information from numerous sources but also to manually compile suitable product copy. This process is not only cumbersome but also makes it difficult for merchants to know which types of users want to learn about or use the product and what their needs are, thus hindering the creation of appropriate content. In the technical solution of this application, data related to the product in the content platform can be analyzed to obtain relevant audiences, scenarios, and product selling points. Several audience-scenario combinations associated with the product can be automatically generated. Then, for the selected audience-scenario combination, corresponding content data (such as relevant scenario descriptions, audience descriptions, product descriptions, etc.) can be intelligently output. This can guide merchants to elaborate on the product around specific audiences and scenarios, transforming abstract product selling points into concrete and perceptible user experiences, thereby enhancing the persuasiveness of the content. Specifically, the content generation guidance scheme based on the interactive flow can include: product demand selection, audience x scenario selection, scenario-based content preview and editing, and content publishing or saving. Each step dynamically adjusts the displayed content based on the results of the previous step. This effectively provides merchants with creative inspiration, preventing them from relying on personal experience and facing a blank creation page, thus improving content completion rate and quality.

[0110] Furthermore, a collaborative mechanism between contextualized content and the publishing process can be implemented. For example, requirement parsing information associated with a requirement can exist as a general intermediate result in structured data form, without being bound to a specific content format. This allows for the selection of appropriate content generation or display strategies in different application scenarios. For instance, the same audience-scenario combination can be reused in various content formats such as text and image notes, product descriptions, live streaming scripts, and short video scripts, and its presentation order or expression structure can be adjusted according to the content format without repeatedly performing the correlation calculation between requirements and selling points.

[0111] Therefore, content creation can be transformed from an experience-based activity into a systemic capability, achieving precise matching between product content production and user needs. This significantly improves the efficiency and quality stability of merchants' content production, and possesses good scalability, adapting to various content formats. This significantly lowers the barrier to content creation; merchants no longer need professional experience in user needs or content creation to generate scenario-based content based on product characteristics associated with identified product needs. It also improves content creation efficiency and stability by using structured product characteristics to obtain audience-scenario combinations and generate corresponding content data. This reduces trial-and-error costs, avoids repeated modifications and inefficient creation, and eliminates reliance on a few experienced creators for content quality. Ordinary merchants can quickly, conveniently, and stably produce effective content through intelligent tools.

[0112] Furthermore, for content platforms, this can improve the overall quality of content supply. The platform can guide merchants to produce content around real user needs, improve the matching degree between content and user needs, and realize the large-scale replication of content production capabilities. For example, it can realize the toolization and scaling of high-quality content production capabilities, thereby enhancing the positive cycle between platform content and transaction conversion. Highly matched content improves user interaction and conversion, further feeding back into demand data and forming a virtuous cycle.

[0113] Furthermore, for consumers, publishing content makes it easier to understand the value and usage scenarios of products. Consumers can quickly understand how products meet their needs through contextualized content, rather than relying solely on parameters or advertising descriptions. This reduces decision-making costs, as the content revolves around real needs, reduces information noise, and helps consumers make purchasing decisions more quickly. It also improves the credibility of the content and the match between content and usage expectations. Content based on objective selling points and real usage situations helps reduce cognitive biases, improve post-purchase satisfaction, and increase the transaction conversion rate of products.

[0114] In this embodiment, demand analysis information related to the target product demand can be displayed on the product information browsing page. This target product demand characterizes the purchase demand for the target product, such as the needs of some users when purchasing or learning about the target product. The demand analysis information is determined based on at least one of the following: search records, transaction records, and published content related to the target product. For example, the demand analysis information may include at least one scenario feature, at least one account feature, and at least one product feature related to the target product. For instance, it may be some scenario features (i.e., some applicable scenarios for the target product) determined through the published content of the target product, or some account features (such as the group of users interested in the target product demand when purchasing it) determined through the target product demand, or some product features related to the target product demand determined around the target product. In other words, some product characteristics related to the target product can be determined through online content analysis. This allows for a response to content generation operations triggered on the product information browsing page. The content editing page displays at least one generation method related to the demand analysis information. This means that one or more generation methods can be derived from the demand analysis information. For example, each generation method can be associated with at least one scenario feature and at least one account feature. This allows for the intelligent generation of content related to the scenario and account features. For instance, based on the generation method, content text related to the target product can be generated more conveniently around the scenario and account features. This not only improves content generation efficiency and convenience but also generates higher-quality content based on the product characteristics analyzed from the target product's information. This content is more closely aligned with the target product (e.g., incorporating descriptions that appeal to a specific group or combining product features that attract users under specific purchasing needs). This enhances the attractiveness of the published content to users, thereby improving the effectiveness of product notes and product conversion rates.

[0115] Based on the above description, this application proposes an information processing method, which can be executed by the aforementioned electronic device, specifically... Figure 1 The terminal device shown. Please refer to [link / reference]. Figure 5 , Figure 5 This is a schematic flowchart of an information processing method provided in an embodiment of this application. For example... Figure 5 As shown, the flow of the information processing method in this application embodiment may include the following: S201. Display the product information browsing page for the target product, and display demand analysis information related to the target product's demand on the product information browsing page. The specific implementation of step S201 can be found in the relevant descriptions of the above embodiments, and will not be repeated here.

[0116] S202. In response to the configuration operation for the generation method of the demand parsing information on the product information browsing page, the publishing content editing page is displayed, and the configuration generation method indicated by the generation method configuration operation is displayed on the publishing content editing page.

[0117] Specifically, in response to a content generation operation triggered on the product information browsing page, at least one generation method related to the demand analysis information can be displayed on the published content editing page. This generation method can be obtained by combining at least one scene feature and at least one account feature. For example, when performing a content generation operation, at least one scene feature and at least one account feature can be automatically combined to obtain a generation method for the user to choose from.

[0118] Optionally, the generation method can also be configured directly by the target account. For example, at least one generation method includes a configuration generation method, where users can select the required parsing information on the product information browsing page and obtain the configuration generation method based on the selected required parsing information (such as scene characteristics, account characteristics, etc.).

[0119] The generation method configuration operation is used to select one or more of the following information from the requirements analysis information: at least one scene feature, at least one account feature, at least one product feature, and at least one piece of published content related to the target product. These information are then combined to obtain the configuration generation method. For example, the configuration generation method can be one scene feature and one account feature, or one scene feature, one account feature, and at least one product feature. Alternatively, the configuration generation method can also be a piece of published content (such as a second piece of published content selected from the creation reference, so that the target model can use the content data of the second piece of published content as a reference and generate corresponding content data based on other selected related features).

[0120] S203. In response to the confirmation operation for the configuration generation method on the content editing page, the content data generated based on the configuration generation method is displayed on the content editing page to publish content related to the target product via the content data.

[0121] When displaying the content editing page, the configuration generation method can be directly displayed (optionally, other generation methods can also be displayed that combine at least one scene feature and at least one account feature to obtain at least one generation method for recommendation).

[0122] At this point, when confirming the configuration generation method, the configuration generation method can be sent to the server so that the server can generate content data related to the target product based on the target model and the configuration generation method. The terminal device can then populate the content data into the content editing page. Subsequently, the content can be published based on the content data on the content editing page and the content related to the target product.

[0123] For example, such as Figures 6a-6b As shown, Figures 6a-6b This is a schematic diagram illustrating a configuration scenario for a generation method provided in an embodiment of this application; wherein, in Figure 6a In the process, you can perform the generation method configuration operation on the product information browsing page. For example, you can display the method configuration information block 60a. In the method configuration information block, you can select at least one scene feature (i.e., content tag, such as "low-fat afternoon tea"), at least one account feature (i.e., audience profile, such as "fitness and body shaping audience"), at least one product feature (i.e., associated product selling points, such as "low glycemic index, 0 fat"), or select at least one second published content. The generation method is configured by combining information based on the selected one or more types of information, thereby improving the flexibility of content generation and allowing you to configure the required generation method yourself.

[0124] At this time Figure 6b After executing the generation method configuration operation and obtaining the configured generation method, the published content editing page can be displayed. This page can display the configured generation method, and optionally, other intelligently combined generation methods can also be displayed.

[0125] At this point, by confirming the configuration generation method on the content editing page, content data matching the configuration generation method can be displayed on the content editing page. This may include descriptions related to the selected characteristics (e.g., if the selected account characteristic is "office worker," an account description containing that characteristic can be generated, such as "particularly suitable for office workers to eat on the go"), or content data generated based on the selected content to be published (e.g., similar content structure).

[0126] In this embodiment, demand analysis information related to the target product's demand can be displayed on the product information browsing page. This target product demand is used to characterize the purchase demand for the target product, such as the needs of some users when purchasing or learning about the target product. For example, the demand analysis information can include at least one scenario feature, at least one account feature, and at least one product feature related to the target product. That is, some product characteristics related to the target product can be determined through online content analysis. In response to selecting a specified product feature on the product information browsing page, a configuration generation method can be obtained. Then, the configuration generation method related to the demand analysis information can be displayed on the content editing page. The content to be published can conform to the target product demand through the configuration generation method. For example, based on the configuration generation method, some content text related to the target product can be generated more conveniently around the specified scenario features and account features. This not only improves the efficiency and convenience of content generation, but also generates higher-quality content based on the product characteristics obtained through the analysis of the target product's relevant information. This content is more in line with the target product's published content (such as incorporating descriptions that are of interest to a certain group, or combining some product features that are more attractive to users under specified purchase needs). This can improve the attractiveness of the published content to users, thereby improving the publishing effect of product notes and the product conversion rate.

[0127] Based on the above description, this application proposes an information processing method, which can be executed by the aforementioned electronic device, specifically... Figure 1 The terminal device shown. Please refer to [link / reference]. Figure 7 , Figure 7 A flowchart illustrating an information processing method provided in this application embodiment. Figure 3 .like Figure 7 As shown, the flow of the information processing method in this application embodiment may include the following: S301. In response to the content generation operation performed on the target product, display the published content editing page.

[0128] One way to access the content editing page is through content generation. This can be done by browsing product information on a product page, or by triggering the content generation action on the target product's viewing page (e.g., the target account can perform content generation on the target product's management page, or trigger the content publishing control on the content platform's homepage to access the content editing page for publishing the corresponding content; the target account can then select the product link to display the target product on the content editing page; or, after triggering the content publishing control, the target account can first select the target product and then access the content editing page with the target product displayed). At this point, the target product can be automatically displayed on the content editing page, or it can be manually displayed.

[0129] S302, In response to a trigger operation on the smart generation entry, display at least one generation method.

[0130] The content editing page includes a smart generation entry point. This smart generation entry point can be triggered automatically when entering the content editing page or manually; there is no limitation on this.

[0131] Each generation method is associated with at least one scenario feature and at least one account feature; both the scenario feature and the account feature are determined based on published content related to the target product. Optionally, for example, the scenario feature may be determined based on published content related to the target product, and the account feature may be determined based on at least one of the following: search records, transaction records, and published content related to the target product.

[0132] Each generation method can also be associated with the product characteristics of the target product, which may or may not be displayed on the content editing page.

[0133] The at least one scene feature and at least one account feature associated with the at least one generation method are obtained from the product requirements of the target product category of the target product, as can be seen in the relevant description of the above embodiments.

[0134] If the content editing page is accessed through the product information browsing page, then at least one generation method displayed on the content editing page is obtained by combining the demand parsing information of the target product demand displayed on the product information browsing page.

[0135] If the content editing page is accessed through the target product's related viewing page, then a requirement guidance prompt can be displayed on the content editing page in association with the target product to view the product information browsing page. When viewing the target product requirement on the product information browsing page, if the user returns to the content editing page, at least one generation method displayed on the content editing page will be obtained from the requirement parsing information of the target product requirement.

[0136] Alternatively, if the content editing page is accessed through the relevant viewing page of the target product, and the target account triggers the smart generation entry to view at least one generation method, the at least one generation method can be determined by the server based on the method determination strategy. For example, it can be obtained by combining the demand parsing information of popular product demands in at least one product demand of the target product category.

[0137] For example, a generation method can be derived by combining popular scenario features and account features from the most frequently requested products, and this generation method can automatically associate with at least one product feature from the popular product requests. Alternatively, it can be determined based on popular generation methods, such as the generation method most frequently used by merchant accounts for a specific product category. No further limitations are imposed here.

[0138] Optionally, in addition to selecting a target generation method from at least one generation method, users can also edit the generation method themselves. For example, a generation method input box can be displayed on the content editing page. In response to an input operation in the generation method input box, the input information can be displayed in the box, and the input information in the box can be designated as a reference generation method (such as user-defined scene features, account features, product features, etc.). Then, in response to a confirmation operation for the reference generation method, the target content for the target product can be generated based on the scene features and account features indicated by the reference generation method.

[0139] Furthermore, the generation method input box can also be used to supplement the selected target generation method. For example, a generation method input box is displayed on the content editing page, and this box is used to enter supplementary generation methods. In this case, in response to the selection operation of the target generation method among at least one generation method, the target content of the target product can be generated based on the target scene features and target account features associated with the target generation method, as well as the supplementary generation methods.

[0140] For example, this input supplement generation method can be used to supplement more content generation conditions (such as adding scene features, audience features, product features, or adding content generation requirements, such as "three paragraphs are required, and the total number of words should not exceed 300 words"), which can make the generation of content data more flexible.

[0141] S303, In response to the selection operation of the target generation method among at least one generation method, generate the target publication content of the target product based on the target scene features and target account features associated with the target generation method.

[0142] Among them, a target generation method can be selected in at least one generation method, so that content data related to the target product can be obtained based on the target scene features and target account features (and product features) associated with the target generation method.

[0143] For example, content data can be generated by combining information such as target scene characteristics, target account characteristics, and product characteristics. Optionally, it can also be combined with published content (such as content selected as a reference by the target account). Optionally, it can also be combined with product images, such as product images uploaded by the target account on the content editing page. Optionally, it can also be combined with product data, that is, content data generated for the target product must use the product data of the target product (such as the product name, product category, etc.), which is not limited here.

[0144] Therefore, in response to the selection operation of the target generation method among at least one generation method, a content generation request for the target product can be generated; the content generation request carries the target generation method and the product image of the target product entered on the content editing page.

[0145] This involves sending a content generation request to the server, which then generates a content generation prompt text for the target product based on the target scene features, target account features, product images, and product data. The prompt text is then input into the target model, which generates the content data for the target published content based on the prompt text.

[0146] This means that content can be generated based on the information indicated by the target generation method to generate prompt text, which is then input into the target model. The target model then intelligently generates content data (such as relevant descriptive information) related to the target product's needs, in order to convey to the user the scenarios in which the target product can be used, the target audience, and its selling points.

[0147] Optionally, there can be one or more target generation methods. For example, multiple target generation methods can be selected to generate multiple pieces of content data simultaneously for the target account to choose from. For instance, the target generation method can be either the first generation method or the second generation method among at least one generation method.

[0148] In response to the selection operation of the first generation method and the second generation method, first content data related to the target product can be generated based on the scene features and account features associated with the first generation method, and second content data related to the target product can be generated based on the scene features and account features associated with the second generation method.

[0149] Therefore, in response to a selection operation on either the first content data or the second content data, the selected content data can be entered in the content input box on the content editing page; the selected content data is used to publish the target content.

[0150] Optionally, the first and second content data can be edited while viewing them. For example, the first content data can be edited while comparing it with the second content data. When confirming the edited first content data, it can be filled into the content editing page, thereby improving the flexibility and diversity of content generation and thus improving content quality.

[0151] For example, such as Figures 8a-8b As shown, Figures 8a-8b This application provides a schematic diagram of a content generation scenario; wherein, in Figure 8a In the middle, on the content editing page, you can attach the target product and enter the product image. At this time, triggering the smart generation entry 80a can display at least one generation method (such as scene x group). At this time, the generation method input box 80b can also be displayed. In this generation method input box, you can enter a reference generation method or a supplementary generation method.

[0152] Among them, you can choose the target generation method (such as method one) to generate and display the content data that meets the needs of the target product based on the target generation method (for example, if the content data is the main text, it can be entered into the main text area 80c on the content editing page). Then, you can publish content related to the target product based on the content data.

[0153] exist Figure 8bIn addition, multiple generation methods can be selected as target generation methods in at least one generation method (such as method one and method two). At this time, multiple content data obtained based on each target generation method can be generated and displayed respectively. Thus, specified content data can be selected to fill the published content editing page, such as displaying two text boxes, such as text box 81a and text box 81b, and displaying content data in the text boxes in sequence. For example, for the content data of method one and the content data of method two, the content can be edited in the text boxes. For example, each text box is associated with a confirmation control. When the confirmation control associated with text box 81a is triggered, the content data of method one can be displayed in the main text area of ​​the published content editing page.

[0154] Compared to traditional methods where merchants can only configure the type of content (e.g., new product launch, flash sale) and product parameters (e.g., "warmth," "comfortable fabric") when publishing product information, the intelligently generated content still focuses on objective information about the product. Users struggle to understand the product's user experience from this content, thus failing to attract them to learn more. However, the technical solution in this application displays multiple generation methods for the target product during intelligent content generation. These methods can be linked to scenario characteristics representing the product's usage scenarios and account characteristics representing the target audience. This allows for the generation of content tailored to specific scenarios or needs, enhancing the richness of the content and enabling users to perceive the product's specific usage. This effectively attracts users with such needs to view and purchase the product through the generated content.

[0155] In this embodiment, in response to a content generation operation performed on a target product, a content editing page can be displayed. This content editing page includes a smart generation entry point and, in response to a trigger operation on the smart generation entry point, displays at least one generation method. Each generation method is associated with at least one scene feature and at least one account feature, and can be determined based on at least one of the following: search records, transaction records, and published content related to the target product. That is, some product characteristics related to the target product can be determined through online content analysis. Then, in response to a selection operation on the target generation method among the at least one generation method, target published content for the target product can be generated. The content data can include descriptive content related to target scenario features and target account features associated with the target generation method. When a user views the target content, if they are interested in the target product based on the descriptive content related to the target scenario features and target account features, they can view or purchase the target product. This not only improves the efficiency and convenience of content generation but also generates higher-quality content based on the product characteristics analyzed from the target product's information. This content is more closely aligned with the target product's published content (e.g., incorporating attractive product features), thereby increasing the appeal of the published content to users and improving the effectiveness of product notes and product conversion rates.

[0156] Based on the above description, this application proposes an information processing method, which can be executed by the aforementioned electronic device, specifically... Figure 1 The terminal device shown. Please refer to [link / reference]. Figure 9 , Figure 9 This is a flowchart illustrating an information processing method provided in an embodiment of this application. (See diagram four.) Figure 9 As shown, the flow of the information processing method in this application embodiment may include the following: S401. The server obtains the published content related to the target product category, performs requirement parsing on the published content related to the target product, obtains at least one product requirement related to the target product category and the requirement description information of each product requirement, and obtains the target published content related to the target product requirement from the published content related to the target product category.

[0157] S402. The server extracts at least one scene feature under the target product requirement based on the scene description in the target published content, extracts at least one account feature under the target product requirement based on the account description in the target published content, and extracts at least one product feature under the target product requirement based on the product description in the target published content, and uses at least one scene feature, at least one account feature and at least one product feature as the requirement parsing information for the target product requirement.

[0158] S403. The terminal device displays a product information browsing page for the target product.

[0159] S404. In response to the category selection operation for the target product category on the product information browsing page, the terminal device requests at least one product requirement related to the target product category from the server.

[0160] S404. In response to the selection operation for the target product demand, the terminal device requests demand parsing information related to the target product demand from the server.

[0161] S404. The terminal device displays demand analysis information related to the target product demand on the product information browsing page.

[0162] S405. In response to a content generation operation triggered on a product information browsing page, the terminal device requests the server for page data of the content editing page and at least one generation method obtained by combining the demand parsing information.

[0163] S406. The terminal device displays at least one generation method related to the demand parsing information on the content editing page, and in response to the selection operation of the target generation method among the at least one generation method, generates a content generation request for the target product and sends the content generation request to the server.

[0164] S407. The server generates content generation prompt text for the target product based on the target scene features and target account features associated with the target generation method, as well as the product image and product data of the target product entered on the content editing page. The server then inputs the content generation prompt text into the target model, which generates the content data of the target published content based on the content generation prompt text.

[0165] S408, The server returns the content data to the terminal device.

[0166] S409. The terminal device enters the received content data in the content input box on the content editing page, and in response to the content publishing operation on the content data, instructs the server to publish the target content of the target product based on the content data.

[0167] In this embodiment, demand analysis information related to the target product demand can be displayed on the product information browsing page. This target product demand characterizes the purchase demand for the target product, such as the needs of some users when purchasing or learning about the target product. The demand analysis information is determined based on at least one of the following: search records, transaction records, and published content related to the target product. For example, the demand analysis information may include at least one scenario feature, at least one account feature, and at least one product feature related to the target product. For instance, it may be some scenario features (i.e., some applicable scenarios for the target product) determined through the published content of the target product, or some account features (such as the group of users interested in the target product demand when purchasing it) determined through the target product demand, or some product features related to the target product demand determined around the target product. In other words, some product characteristics related to the target product can be determined through online content analysis. This allows for a response to content generation operations triggered on the product information browsing page. The content editing page displays at least one generation method related to the demand analysis information. This means that one or more generation methods can be derived from the demand analysis information. For example, each generation method can be associated with at least one scenario feature and at least one account feature. This allows for the intelligent generation of content related to the scenario and account features. For instance, based on the generation method, content text related to the target product can be generated more conveniently around the scenario and account features. This not only improves content generation efficiency and convenience but also generates higher-quality content based on the product characteristics analyzed from the target product's information. This content is more closely aligned with the target product (e.g., incorporating descriptions that appeal to a specific group or combining product features that attract users under specific purchasing needs). This enhances the attractiveness of the published content to users, thereby improving the effectiveness of product notes and product conversion rates.

[0168] Please see Figure 10 , Figure 10 This is a schematic diagram of the structure of an information processing device provided in an embodiment of this application. It should be noted that... Figure 10 The information processing apparatus shown is used to execute this application. Figure 3 , Figure 5 , Figure 7 , Figure 9 The methods in the illustrated embodiments are shown only in the parts relevant to the embodiments of this application for ease of explanation; specific technical details are not disclosed. Reference to this application is required. Figure 3 , Figure 5 , Figure 7 , Figure 9 The example shown. Figure 10The example information processing device 1000 may include: an information display module 1001 and a page display module 1002. Wherein: The information display module 1001 is used to display the product information browsing page of the target product. The product information browsing page displays demand analysis information related to the target product demand. The demand analysis information is determined based on the published content related to the target product. The demand analysis information includes at least one scenario feature, at least one account feature and at least one product feature related to the target product. The page display module 1002 is used to respond to the content generation operation triggered on the product information browsing page and display at least one generation method related to the demand analysis information on the published content editing page; when each generation method is associated with at least one scene feature and at least one account feature, each generation method is used to intelligently generate published content related to the scene feature and the account feature.

[0169] Specifically, when the information display module 1001 is used to display demand parsing information related to the target product demand on the product information browsing page, it is used for: In response to the category selection operation for the target product category on the product information browsing page, at least one product requirement related to the target product category is filtered and displayed on the product information browsing page; the at least one product requirement includes the target product requirement; When responding to a selection action for a target product, display demand analysis information related to the target product demand.

[0170] Specifically, when displaying the product information browsing page for the target product, the information display module 1001 is used for: Displays the content editing page; When a target product is displayed on the content editing page, the target product category is determined, and demand guidance information for the target product category is displayed. The demand guidance information is used to view at least one product demand related to the target product category that contains the demand for the target product. In response to a prompt message guiding your needs, display a product information browsing page that includes at least one product requirement.

[0171] The product information browsing page displays account feature information blocks, with one account feature information block corresponding to one account feature; Page display module 1002 is also used for: In response to a trigger operation targeting an account feature information block, the account details page corresponding to the target account feature of the triggered account feature information block is displayed. Display the characteristic details of the target account on the account details page, and / or the first published content related to the target account characteristics in the published content related to the target product.

[0172] Among them, the content published related to the target product includes multiple secondary publications associated with each scenario feature; The information display module 1001, when used to display demand parsing information related to the target product's demand on the product information browsing page, is specifically used for: On the product information browsing page, at least one second published content associated with at least one scene feature is displayed, and scene tags indicating the corresponding associated scene feature are displayed on each second published content.

[0173] The page display module 1002 is also used for: In response to a trigger operation on a scene tag on any of the second published content, display a scene details page showing the target scene features corresponding to the triggered scene tag; The scene details page displays multiple secondary publications associated with the target scene features.

[0174] Each generation method is composed of at least one scene feature and at least one account feature, and is associated with at least one product feature. Page display module 1002 is also used for: In response to the selection of a target generation method among at least one generation method on the content editing page, the content data generated for the target product based on the target generation method is displayed on the content editing page; the content data includes one or more of the following: scene description related to the scene characteristics corresponding to the target generation method, account description related to the account characteristics corresponding to the target generation method, and product description related to the product characteristics associated with the target generation method; In response to content publishing operations on the content editing page, publish target content related to the target product based on content data.

[0175] At least one of the generation methods includes a configuration generation method; When the page display module 1002 responds to a content generation operation triggered on the product information browsing page and displays at least one generation method related to the requirements analysis information on the published content editing page, it is specifically used for: In response to the configuration operation for the generation method of demand parsing information on the product information browsing page, when the configuration generation method indicated by the configuration operation is determined, the published content editing page is displayed, and the configuration generation method is displayed on the published content editing page; The generation method configuration operation is used to select one or more of the following information from the requirements analysis information: at least one scenario feature, at least one account feature, at least one product feature, and at least one piece of published content related to the target product, and combine the information to obtain the configuration generation method.

[0176] Among them, the published content related to the target product includes target published content related to the demand for the target product, at least one scenario feature is extracted based on the scenario description in the target published content, at least one account feature is extracted based on the account description in the target published content, the search account corresponding to the search record, and the transaction account corresponding to the transaction record, and at least one product feature refers to the product feature related to the demand for the target product extracted based on the product description in the target published content. The content published related to the target product includes at least one of the following: content published with keywords related to the target product, or content published with the target product attached.

[0177] The specific implementation methods of the information display module and the page display module can be found in the description of the above embodiments, and will not be repeated here. It should be understood that the beneficial effects obtained by using the same method will also not be repeated here.

[0178] Please see Figure 11 , Figure 11 This is a schematic diagram of the structure of an information processing device provided in an embodiment of this application. It should be noted that... Figure 11 The information processing apparatus shown is used to execute this application. Figure 3 , Figure 5 , Figure 7 , Figure 9 The methods in the illustrated embodiments are shown only in the parts relevant to the embodiments of this application for ease of explanation; specific technical details are not disclosed. Reference to this application is required. Figure 3 , Figure 5 , Figure 7 , Figure 9 The example shown. Figure 11 The example information processing device 1100 may include: an editing page display module 1101 and a content generation module 1102. Wherein: The editing page display module 1101 is used to display the published content editing page in response to the content generation operation performed on the target product; wherein, the published content editing page includes an intelligent generation entry point; The editing page display module 1101 is also used to respond to the trigger operation of the intelligent generation entry and display at least one generation method; wherein each generation method is associated with at least one scene feature and at least one account feature; wherein the scene feature and the account feature are both determined based on the published content related to the target product; The content generation module 1102 is used to generate target content for the target product based on the target scene features and target account features associated with the target generation method in response to the selection operation of the target generation method among at least one generation method.

[0179] The content generation module 1102, in response to a selection operation for a target generation method among at least one generation method, generates target content for a target product based on the target scene features and target account features associated with the target generation method, specifically for: In response to the selection operation for the target generation method among at least one generation method, a content generation request for the target product is generated; the content generation request carries the target generation method and the product image of the target product entered on the content editing page. The content generation request is sent to the server, which then generates content generation prompt text for the target product based on the target scene features, target account features, product image, and product data of the target product. The content generation prompt text is then input into the target model, which generates the content data for the target published content based on the content generation prompt text.

[0180] The editing page display module 1101 is also used for: Display a box for entering the generation method on the content editing page; In response to an input operation in the generation method input box, the input information is displayed in the generation method input box, and the input information in the generation method input box is determined as the reference generation method. In response to the confirmation operation for the reference generation method, target publication content for the target product is generated based on the scene characteristics and account characteristics indicated by the reference generation method.

[0181] The content editing page displays a generation method input box, which is used to enter supplementary generation methods. When the content generation module 1102 generates target content for a target product based on the target scene features and target account features associated with the target generation method in response to a selection operation for a target generation method among at least one generation method, it is specifically used for: In response to the selection operation of the target generation method among at least one generation method, the target publishing content of the target product is generated based on the target scene features and target account features associated with the target generation method, as well as the supplementary generation method.

[0182] The target generation method is either the first generation method or the second generation method among at least one generation method; When the content generation module 1102 generates target content for a target product based on the target scene features and target account features associated with the target generation method in response to a selection operation for a target generation method among at least one generation method, it is specifically used for: In response to the selection operation for the first generation method and the second generation method, first content data related to the target product is generated based on the scene features and account features associated with the first generation method, and second content data related to the target product is generated based on the scene features and account features associated with the second generation method. In response to a selection operation on either the first content data or the second content data, the selected content data is entered in the content input box on the content editing page; the selected content data is used to publish the target content.

[0183] The specific implementation methods of the editing page display module and the content generation module can be found in the description of the above embodiments, and will not be repeated here. It should be understood that the beneficial effects obtained by using the same method will also not be repeated here.

[0184] Please see Figure 12 , Figure 12 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. Figure 12 As shown, the electronic device 2600 includes at least one processor 2601 and a memory 2602. Optionally, the electronic device may also include a network interface. The processor 2601, memory 2602, and network interface can exchange data. The network interface, controlled by the processor 2601, is used to send and receive messages. The memory 2602 stores computer programs, including program instructions. The processor 2601 executes the program instructions stored in the memory 2602. The processor 2601 is configured to invoke the program instructions to execute the aforementioned method. The memory 2602 may include volatile memory, such as random-access memory (RAM); the memory 2602 may also include non-volatile memory, such as flash memory, solid-state drive (SSD), etc.; the memory 2602 may also include combinations of the above types of memory.

[0185] Processor 2601 may be a central processing unit (CPU). In one embodiment, processor 2601 may also be a graphics processing unit (GPU). Processor 2601 may also be a combination of a CPU and a GPU. Processor 2601 may be used to invoke device control applications stored in memory 2602 to perform the above-described tasks. Figure 3 , Figure 5 , Figure 7 , Figure 9 The description of the information processing method in the corresponding embodiments can also be executed as described above. Figures 10-11 The description of the information processing apparatus in the corresponding embodiments will not be repeated here. Furthermore, the beneficial effects of using the same method will also not be repeated.

[0186] In specific implementations, the devices, processors, memory, etc., described in the embodiments of this application can execute the implementation methods described in the above method embodiments, or they can execute the implementation methods described in the embodiments of this application, which will not be repeated here.

[0187] This application also provides a computer-readable storage medium storing a computer program. The computer program includes program instructions, which, when executed by a processor, enable the processor to perform some or all of the steps described in the above method embodiments. Optionally, the computer storage medium can be volatile or non-volatile. The computer-readable storage medium may primarily include a program storage area and a data storage area. The program storage area may store an operating system, at least one application program required for a given function, etc.; the data storage area may store data created based on the use of blockchain nodes, etc.

[0188] This application provides a computer program product, which may include a computer program. When the computer program is executed by a processor, it can implement some or all of the steps in the above method, which will not be elaborated here.

[0189] In this article, "multiple" refers to two or more. "And / or" describes the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A alone, A and B simultaneously, or B alone. The character " / " generally indicates that the preceding and following related objects have an "or" relationship.

[0190] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. This computer program can be stored in a computer storage medium, which can be a computer-readable storage medium. When executed, the program can include the processes of the embodiments of the methods described above. The storage medium can be a magnetic disk, optical disk, read-only memory (ROM), or random access memory (RAM), etc.

[0191] The above-disclosed embodiments are merely some of the embodiments of this application, and should not be construed as limiting the scope of this application. Those skilled in the art can understand that all or part of the processes for implementing the above embodiments, and equivalent changes made in accordance with the claims of this application, still fall within the scope of this application.

Claims

1. An information processing method, characterized in that, The method includes: The product information browsing page of the target product is displayed, and demand analysis information related to the target product demand is displayed on the product information browsing page; the demand analysis information is determined based on the published content related to the target product, and the demand analysis information includes at least one scenario feature, at least one account feature and at least one product feature related to the target product; In response to a content generation operation triggered on the product information browsing page, at least one generation method related to the demand parsing information is displayed on the published content editing page; when each generation method is associated with at least one scene feature and at least one account feature, each generation method is used to intelligently generate published content related to the scene feature and the account feature.

2. The method according to claim 1, characterized in that, The process of displaying demand parsing information related to the target product's demand on the product information browsing page includes: In response to a category selection operation for the target product category on the product information browsing page, at least one product requirement related to the target product category is filtered and displayed on the product information browsing page; the at least one product requirement includes the target product requirement; In response to the selection operation for the target product demand, demand parsing information related to the target product demand is displayed.

3. The method according to claim 1, characterized in that, The product information browsing page displaying the target product includes: Display the content editing page; When the target product is displayed on the content editing page, the target product category is determined, and demand guidance information for the target product category is displayed; the demand guidance information is used to view at least one product demand related to the target product category that contains the demand for the target product. In response to the triggered operation of the demand guidance prompt information, a product information browsing page containing the demand for at least one product is displayed.

4. The method according to claim 1, characterized in that, The product information browsing page displays account feature information blocks, with one account feature information block corresponding to one account feature. The method further includes: In response to a triggering operation on the account feature information block, the account details page for the target account feature corresponding to the triggered account feature information block is displayed; The feature details of the target account are displayed on the account details viewing page, and / or the first published content related to the target account features in the published content related to the target product.

5. The method according to claim 1, characterized in that, The content published related to the target product includes multiple second published contents associated with each scenario feature; The process of displaying demand parsing information related to the target product's demand on the product information browsing page includes: On the product information browsing page, at least one second published content associated with the at least one scene feature is displayed, and scene tags indicating the corresponding associated scene feature are displayed on each second published content.

6. The method according to claim 5, characterized in that, The method further includes: In response to a trigger operation on a scene tag on any of the second published content, display a scene details page showing the target scene features corresponding to the triggered scene tag; The scene details page displays multiple second-release content items associated with the target scene features.

7. The method according to claim 1, characterized in that, Each of the above-mentioned generation methods is obtained by combining at least one scene feature and at least one account feature, and is associated with at least one product feature; The method further includes: In response to the selection operation of the target generation method among the at least one generation method on the published content editing page, the published content editing page displays content data generated for the target product based on the target generation method; the content data includes one or more of the following: scene description related to the scene features corresponding to the target generation method, account description related to the account features corresponding to the target generation method, and product description related to the product features associated with the target generation method; In response to the content publishing operation on the published content editing page, target published content related to the target product is published based on the content data.

8. The method according to claim 1, characterized in that, The at least one generation method includes a configuration generation method; The response to the content generation operation triggered on the product information browsing page, displaying at least one generation method related to the demand parsing information on the published content editing page, includes: In response to the configuration operation for the generation method of the demand parsing information on the product information browsing page, when the configuration generation method indicated by the configuration operation is determined, the published content editing page is displayed, and the configuration generation method is displayed on the published content editing page; The generation method configuration operation is used to select one or more of the following information from the demand parsing information: at least one scenario feature, at least one account feature, at least one product feature, and at least one published content related to the target product, and combine them to obtain the configuration generation method.

9. The method according to claim 1, characterized in that, The target product-related published content includes target published content related to the demand for the target product. The at least one scenario feature is extracted based on the scenario description in the target published content. The at least one account feature is extracted based on the account description in the target published content. The at least one product feature refers to product features related to the demand for the target product extracted based on the product description in the target published content. The content published related to the target product includes at least one of the following: content published with keywords related to the target product, and content published with the target product attached.

10. An information processing method, characterized in that, The method includes: In response to the content generation operation performed on the target product, a content editing page is displayed; wherein, the content editing page includes a smart generation entry point; In response to a trigger operation on the intelligent generation entry point, at least one generation method is displayed; wherein each generation method is associated with at least one scene feature and at least one account feature; wherein the scene feature and the account feature are both determined based on the published content related to the target product; In response to the selection operation of the target generation method among the at least one generation method, the target publishing content of the target product is generated based on the target scene features and target account features associated with the target generation method.

11. The method according to claim 10, characterized in that, The step of responding to the selection operation of a target generation method among the at least one generation method, generating target publishing content for the target product based on target scene features and target account features associated with the target generation method, includes: In response to a selection operation for a target generation method among the at least one generation methods, a content generation request for the target product is generated; the content generation request carries the target generation method and a product image of the target product entered on the published content editing page; The content generation request is sent to the server, so that the server generates content generation prompt text for the target product based on the target scene features, the target account features, the product image, and the product data of the target product. The content generation prompt text is then input into the target model, which generates the content data of the target published content based on the content generation prompt text.

12. The method according to claim 10, characterized in that, The method further includes: A generation method input box is displayed on the content editing page. In response to an input operation in the generation method input box, the input information is displayed in the generation method input box, and the input information in the generation method input box is determined to be a reference generation method. In response to the confirmation operation for the reference generation method, the target publication content of the target product is generated based on the scene features and account features indicated by the reference generation method.

13. The method according to claim 10, characterized in that, The content editing page displays a generation method input box, which is used to input supplementary generation methods. The step of responding to the selection operation of a target generation method among the at least one generation method, generating target publishing content for the target product based on target scene features and target account features associated with the target generation method, includes: In response to the selection operation of the target generation method among the at least one generation method, the target publishing content of the target product is generated based on the target scene features and target account features associated with the target generation method, and the supplementary generation method.

14. The method according to claim 10, characterized in that, The target generation method is the first generation method and the second generation method among the at least one generation method; The step of responding to the selection operation of a target generation method among the at least one generation method, generating target publishing content for the target product based on target scene features and target account features associated with the target generation method, includes: In response to the selection operation for the first generation method and the second generation method, first content data related to the target product is generated based on the scene features and account features associated with the first generation method, and second content data related to the target product is generated based on the scene features and account features associated with the second generation method. In response to a selection operation for either the first content data or the second content data, the selected content data is entered in the content input box on the published content editing page; the selected content data is used to publish the target published content.

15. An information processing device, characterized in that, The device includes: An information display module is used to display a product information browsing page for a target product, and to display demand analysis information related to the target product's demand on the product information browsing page; the demand analysis information is determined based on the published content related to the target product, and the demand analysis information includes at least one scenario feature, at least one account feature, and at least one product feature related to the target product; The page display module is used to respond to the content generation operation triggered on the product information browsing page and display at least one generation method related to the demand parsing information on the published content editing page; when each generation method is associated with at least one scene feature and at least one account feature, each generation method is used to intelligently generate published content related to the scene feature and the account feature.

16. An information processing device, characterized in that, The device includes: The editing page display module is used to display the published content editing page in response to the content generation operation performed on the target product; wherein, the published content editing page includes an intelligent generation entry point; The editing page display module is also used to display at least one generation method in response to a trigger operation on the intelligent generation entry; wherein each generation method is associated with at least one scene feature and at least one account feature; wherein the scene feature and the account feature are both determined based on the published content related to the target product; The content generation module is used to respond to the selection operation of the target generation method among the at least one generation method, and generate the target publication content of the target product based on the target scene features and target account features associated with the target generation method.

17. An electronic device, characterized in that, The system includes a processor and a memory, wherein the memory is used to store a computer program, the computer program including program instructions, and the processor is configured to invoke the program instructions to perform the method as described in any one of claims 1-14.

18. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program, the computer program including program instructions that, when executed by a processor, cause the processor to perform the method as described in any one of claims 1-14.

19. A computer program product, characterized in that, The method includes a computer program comprising program instructions that, when executed by a processor, implement the method according to any one of claims 1-14.

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