Ecological method and system for online learning and entrepreneurship and employment in e-commerce industry

Abstract

The invention belongs to the technical field of entrepreneurship and employment ecological methods, and particularly relates to an e-commerce industry online learning and entrepreneurship and employment ecological method and system, and the method comprises the steps: constructing a user portrait through a mixed mode of AI evaluation and artificial review, and carrying out the federal learning processing of portrait data, so as to guarantee the privacy safety; generating a dynamically optimized learning path based on a user portrait and a market real-time demand through a mixed recommendation algorithm integrating collaborative filtering, content recommendation and demand matching in combination with a reinforcement learning mechanism; micro-module, scene-based and generative-design learning content is provided, real-time content updating is realized through a three-party linkage mechanism, and a learning, feedback and optimization closed loop is formed; a virtual e-commerce sand table and a live broadcast training cabin are built to provide a zero-cost virtual actual combat scene, and multi-e-commerce mode full-process simulation and special skill intensified training are supported; a real practical training special area is set up in connection with a mainstream e-commerce platform and high-quality merchants, and compliant and controllable real operation opportunities and commission sharing are provided for users.

Description

Technical Field

[0001] This invention belongs to the technical field of entrepreneurial and employment ecosystem methods, and particularly relates to an online learning and entrepreneurial and employment ecosystem method and system for the e-commerce industry. Background Technology

[0002] The e-commerce industry is undergoing rapid digital transformation, continuously raising the professional skill requirements for practitioners. These requirements encompass capabilities in areas such as operations, live streaming, product selection, and cross-border compliance. However, traditional online e-commerce learning platforms generally suffer from fragmented content and outdated updates. Most courses focus solely on theoretical explanations, lacking integration with real-time market rules and failing to construct highly realistic practical scenarios. This makes it difficult for users to directly translate their learning outcomes into practical skills, resulting in a significant disconnect between learning and application. Furthermore, after completing their studies, learners face challenges such as fragmented access to entrepreneurial resources and a low match between employment channels and job requirements, making it difficult to form a complete closed loop from learning to practice and then to entrepreneurship and employment.

[0003] Furthermore, in the process of starting an e-commerce business, novice entrepreneurs often face product quality risks due to a lack of effective means to verify the qualifications of their supply chains, and the cost of acquiring compliance tools is high. In the job market, companies struggle to quickly verify the authenticity of job seekers' skills, and job seekers lack credible credentials to prove their practical abilities, resulting in low efficiency in the connection between the two parties. The existing system has not formed a collaborative mechanism between learners, enterprises, supply chains, and platforms; data at each stage is isolated, trust costs are high, and it cannot meet the needs of the rapidly developing e-commerce industry for end-to-end empowerment. There is an urgent need for an ecological solution that can integrate learning, practical experience, resource matching, and credible certification. Summary of the Invention

[0004] The purpose of this invention is to address the aforementioned technical problems by providing a method for creating an online learning, entrepreneurship, and employment ecosystem in the e-commerce industry, comprising the following steps: Step 1: A user profile is constructed using a hybrid model of AI assessment and manual review. The profile data is processed through federated learning to ensure privacy and security. Step 2: Based on user profiles and real-time market demand, a hybrid recommendation algorithm that integrates collaborative filtering, content recommendation, and demand matching is used, combined with a reinforcement learning mechanism to generate a dynamically optimized learning path; Step 3: Provide learning content with micro-modules, scenario-based and generative design, and achieve real-time content updates through a three-party linkage mechanism to form a closed loop of learning, feedback and optimization; Step 4: Build a virtual e-commerce sandbox and live streaming training cabin to provide a zero-cost virtual practice scenario, supporting full-process simulation of multiple e-commerce models and special skills enhancement training; Step 5: Connect with mainstream e-commerce platforms and high-quality merchants to set up real training zones to provide users with compliant and controllable real operational opportunities and commission sharing; Step Six: Integrate resources such as supply chain, funding, and compliance to provide precise matching and efficient connection services for entrepreneurial users; Build a full-chain employment service system to achieve precise matching between job seekers and enterprises; Step 7: Introduce blockchain technology to build a trusted authentication system to store and prove information such as user learning outcomes and practical data; Triple protection is employed to safeguard data, content, and transaction security; Step 8: Achieve sustainable ecological operation through an incentive system that combines points, certification, and resource allocation with a win-win model with ecological partners.

[0005] Preferably, the multimodal data in step one includes four categories: basic attributes, career goals, ability dimensions, and behavioral characteristics; the AI ​​assessment uses an adaptive question bank to dynamically adjust the difficulty of the questions, combines natural language processing to analyze open-ended answers, and outputs a report on ability shortcomings; for users with unclear goals, interactive questionnaires and scenario simulation questions are used to generate career suitability analysis.

[0006] Preferably, the learning path optimization described in step two is adjusted in real time based on the user's learning progress, assessment results, and practical feedback; core courses are prioritized for pushing to users with different skill gaps, and advanced content is automatically accessed after basic skills are met.

[0007] Preferably, the tripartite content update mechanism mentioned in step three includes: connecting to the open interfaces of mainstream e-commerce platforms to capture new regulations information, and AI automatically generating interpretation courses; accessing industry data analysis tools to obtain real-time data and updating relevant courses synchronously; and a team of senior industry practitioners and experts providing in-depth insights and AI assisting in the production of courses in various formats.

[0008] Preferably, the virtual e-commerce sandbox in step four adopts a front-end rendering scheme combining WebGL and Unity3D and a back-end design based on microservice architecture. It supports full-process simulation of four major scenarios: traditional shelf e-commerce, live e-commerce, social e-commerce, and cross-border e-commerce, and has different difficulty levels built in. It generates a dynamic market environment through Monte Carlo simulation algorithm, simulates the behavior of virtual audiences and competitors, and outputs data analysis reports.

[0009] Preferably, the live streaming training cabin described in step four supports the construction of custom virtual scenes and collaboration with virtual anchors; Based on NLP and sentiment analysis technology, a multi-feature virtual audience is generated to simulate interaction and unexpected situations, and real-time scores and optimization suggestions are given based on user performance. It features a built-in data analysis module and AI-powered script analysis tools, providing data visualization and script optimization templates.

[0010] Preferably, the real-world training zone described in step five communicates with the training channel of the e-commerce platform via an API interface; after completing the learning assessment at a specified stage, users obtain an exclusive training account with basic operational permissions and are subject to dual control; partner merchants provide drop-shipping products, the e-commerce platform provides small-amount traffic support, the ecosystem provides full guidance, and training data is synchronized to the individual's credit file.

[0011] Preferably, the startup resource matching in step six includes: a supply chain matching module that uses blockchain to store product qualifications and AI matching technology to recommend suitable resources and provide supply chain management tools; a compliance and tool integration module that provides one-click access to various compliance tools and services through API interfaces, and provides them in tiers according to the startup stage; and a funding matching channel that uses credit assessment and intelligent matching to provide special funding support and risk warnings.

[0012] Preferably, the employment service system described in step six includes using NLP technology to analyze enterprise job requirements and construct job profiles, generating job seeker skill certification reports to construct competency profiles; the matching algorithm adopts a three-dimensional model of hard skill matching, soft quality adaptation, and development potential assessment; and provides AI mock interviews, resume optimization tools, and supplementary learning content for onboarding adaptation.

[0013] Preferably, the triple protection in step seven includes storing sensitive information encrypted with AES-256 and transmitting it using HTTPS and SSL encryption; establishing fine-grained access control policies; using federated learning and differential privacy technologies to ensure data privacy; and the trusted authentication system supports users in independently controlling the sharing permissions of digital credentials.

[0014] In view of this, the present invention provides a method and system for online learning, entrepreneurship and employment ecosystem in the e-commerce industry.

[0015] The beneficial effects of this invention are:

[0016] This solution effectively addresses the core pain points of traditional learning platforms—the disconnect between learning and application, and the fragmentation of resources—by building an e-commerce learning, entrepreneurship, and employment ecosystem through the integration of multiple technologies. On one hand, it leverages multimodal data collection and hybrid recommendation algorithms to achieve precise matching between learning content and user and market demands. Combined with micro-module courses and generative AI-customized content, it adapts to fragmented learning scenarios and ensures the timeliness of knowledge. On the other hand, through highly realistic practical scenarios such as virtual e-commerce sandboxes and live-streaming training cabins, as well as real training zones connected to mainstream e-commerce platforms, it forms a progressive path from virtual simulation to semi-real training to real operation. This allows users to accumulate practical experience in a zero-cost, low-risk environment, significantly improving the conversion efficiency of core skills such as e-commerce operations and live-streaming sales, and greatly reducing the cost of entrepreneurial trial and error and the employment adaptation cycle.

[0017] Meanwhile, this solution, through the deep application of blockchain and AI technologies, constructs a trustworthy and efficient collaborative ecosystem. Blockchain-based evidence storage ensures the immutability of learning outcomes, training data, and supply chain qualifications, resolving skills verification challenges in corporate recruitment and supply chain trust risks in entrepreneurship. An AI-driven resource matching mechanism significantly improves the efficiency of entrepreneurial resource matching and the accuracy of job seeker-entrepreneur pairing. Combined with the integration of compliance tools and funding channels, it further lowers the barriers to entry for e-commerce entrepreneurship. Furthermore, a layered technical architecture ensures system flexibility and stability, while user incentives and a win-win partnership model achieve symbiotic prosperity for learners, enterprises, supply chains, and the platform, ultimately forming a data-driven, dynamically optimized, and sustainable ecosystem that effectively improves the quality of talent cultivation and the success rate of entrepreneurship and employment in the e-commerce industry. Attached Figure Description

[0018] Figure 1 This is a flowchart of the method of the present invention. Detailed Implementation

[0019] The technical solutions of the embodiments of this application will be clearly described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of this application. All other embodiments obtained by those skilled in the art based on the embodiments of this application are within the scope of protection of this application.

[0020] It should be noted that all directional and positional terms used in this invention, such as "up," "down," "left," "right," "front," "back," "vertical," "horizontal," "inner," "outer," "top," "lower," "lateral," "longitudinal," and "center," are only used to explain the relative positional relationships and connections between components in a specific state (as shown in the accompanying drawings). They are merely for the convenience of describing the invention and do not require the invention to be constructed and operated in a specific orientation; therefore, they should not be construed as limitations on the invention. Furthermore, descriptions involving "first," "second," etc., are for descriptive purposes only and should not be construed as indicating or implying their relative importance or implicitly specifying the number of technical features indicated.

[0021] In the description of this invention, unless otherwise explicitly specified and limited, the terms "installation," "connection," and "linking" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; and they can refer to the internal communication between two components. Those skilled in the art can understand the specific meaning of the above terms in this invention based on the specific circumstances.

[0022] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "illustrative embodiment," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of the invention. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0023] This solution integrates multiple technologies to achieve a full-chain empowerment that accurately matches learning content with the market, provides highly realistic practical scenarios, efficiently connects entrepreneurial resources, and directly links employment channels to enterprises, ultimately forming a symbiotic ecosystem among learners, enterprises, supply chains, and platforms.

[0024] The technical architecture adopts a cloud-native, microservices, multimodal AI, and blockchain underlying support system, divided into a five-layer architecture: perception layer, data layer, algorithm layer, application layer, and ecosystem layer. Each layer achieves data interoperability and functional collaboration through an API gateway, ensuring the flexibility, scalability, and stability of the ecosystem.

[0025] Once users enter the ecosystem, a comprehensive user profile is built through multimodal data collection and analysis, laying the foundation for personalized learning and subsequent resource integration. Data collection dimensions include four categories: basic attributes (age, education, region, computer skills), career goals (clearly defined employment / entrepreneurship / part-time work, target positions such as operations / live streaming / product selection, target sectors such as traditional e-commerce / cross-border e-commerce / interest e-commerce), ability dimensions (e-commerce basic knowledge, data analysis ability, communication skills, and marketing mindset as assessed through introductory assessments), and behavioral characteristics (learning habits such as fragmented / systematic learning, preferred content formats such as video / text / case studies, intensity of willingness to practice, and expected time investment). The diagnostic technology employs a hybrid model of AI assessment and human review. AI dynamically adjusts the difficulty of questions through an adaptive assessment question bank, and combines natural language processing analysis of users' open-ended answers (such as e-commerce operation plan ideas) to quickly output a report on ability gaps. For users with unclear goals, interactive questionnaires and scenario simulation questions are used (e.g., "Faced with slow-moving products, what measures would you prioritize?"). AI generates career suitability analysis and recommends matching employment / entrepreneurship directions. All user profile data is processed using federated learning technology to maximize data value while protecting user privacy. Based on user profiles and real-time market demands, a hybrid recommendation algorithm generates dynamically adjusted learning paths. The algorithm integrates three core logics: collaborative filtering (referencing successful learning paths of similar target users), content recommendation (matching corresponding courses based on skill gaps), and demand matching (combining enterprise job requirements with popular industry skills). It also incorporates a reinforcement learning mechanism to optimize the path in real time based on user learning progress, assessment results, and practical feedback. For example, if a user's goal is to become a live-streaming e-commerce entrepreneur and the assessment shows a lack of script design and traffic operation skills, the system will prioritize pushing core courses such as live-streaming script writing and Douyin / Kuaishou traffic algorithm analysis. Once basic skills are met, advanced content such as live-streaming team building and supply chain negotiation skills will be automatically introduced.

[0026] The learning content adopts a micro-module, scenario-based, and generative design, breaking down core e-commerce skills into 10- and 20-minute micro-module courses. Each module focuses on a single knowledge point (such as title keyword optimization and live-streaming interactive script design) to adapt to fragmented learning scenarios. The course content uses real e-commerce cases as carriers, replacing traditional theoretical explanations with scenario-based narratives (such as the practical process of a novice live streamer gaining 100,000 followers in 3 months). Generative AI technology is introduced to generate customized cases and exercises based on real-time user learning feedback. For example, after learning about cross-border e-commerce logistics selection, AI can generate logistics solution design questions for different product types (small / light / large / fragile items) and different target markets (Europe / America / Southeast Asia) and provide personalized analysis.

[0027] To address the pain points of rapid knowledge iteration and frequent rule changes in the e-commerce industry, a three-way collaborative content update mechanism has been established. This mechanism consists of three parts: First, connecting to the open interfaces of mainstream e-commerce platforms (Taobao, JD.com, Douyin, Pinduoduo, Amazon, etc.) to capture real-time information on new platform regulations (such as live streaming compliance requirements, cross-border logistics policies), traffic algorithm adjustments, and promotional activities. AI automatically generates interpretation course modules, which are then launched online within 24 hours. Second, integrating with industry data analysis tools (such as Chanmama, QianGua Data, SimilarWeb) to obtain real-time data on popular categories, best-selling product characteristics, and user consumption trends, synchronously updating course content related to product selection, pricing, and promotion. Third, forming a team of e-commerce professionals... A team of experts comprised of seasoned industry professionals (operations directors, experienced entrepreneurs, and platform business development managers) delivers in-depth industry insights and practical skills weekly. AI-assisted production of these courses in various formats, including videos and text, ensures the practicality and forward-looking nature of the learning content. A closed loop of learning, feedback, and optimization is established, allowing users to submit questions and suggestions during the learning process. AI, combined with natural language processing, analyzes frequently asked questions and automatically identifies course content requiring optimization. The system regularly assesses the match between user learning outcomes and market demand. If market demand for a particular skill increases but user mastery is low, the system automatically adjusts course weighting, increasing the recommendation intensity and practice opportunities for related modules.

[0028] The virtual e-commerce sandbox serves as the core practical platform for the ecosystem. Employing 3D rendering, AI data simulation, and multi-role interaction technologies, it recreates the entire operational process of different e-commerce models, enabling users to conduct practical exercises at zero cost and low risk. In terms of technical architecture, the front-end uses a rendering solution combining WebGL and Unity3D to achieve high-definition scene presentation and smooth operation across devices (PC, mobile, and tablet). The back-end is based on a microservice architecture, splitting into multiple modules such as product management, order processing, marketing promotion, customer service, data analysis, and competitor simulation, with real-time communication between modules achieved through message queues. The AI ​​interaction engine uses a multimodal large model to support multiple interaction methods such as text, voice, and images, simulating the behavior of multiple roles, including users, competitors, and platform regulators.

[0029] In terms of scenario coverage, the simulation platform supports full-process simulation of four major e-commerce scenarios: traditional shelf e-commerce (product selection, listing, title optimization, direct traffic promotion, customer service communication, and after-sales handling), live-streaming e-commerce (scenario setup, script design, product explanation, interactive control, traffic placement, and order conversion), social e-commerce (community operation, content seeding, distribution management, and user growth), and cross-border e-commerce (product selection research, overseas warehouse selection, customs declaration and inspection, multilingual customer service, currency conversion, and compliant operation). Each scenario has different difficulty levels (beginner, intermediate, and advanced) that users can choose freely according to their learning progress.

[0030] In terms of data generation logic, the sandbox is based on real e-commerce industry data (such as gross profit margins for each category, traffic conversion funnels, and user consumption behavior characteristics) and generates a dynamic market environment through Monte Carlo simulation algorithms. For example, after a user selects the live-streaming e-commerce scenario, AI will generate virtual viewers that match the characteristics of the target audience (the proportion of users of different ages, spending power, and purchase intentions) and simulate the audience's questioning, ordering, and return behaviors in real time; at the same time, it will generate 3 to 5 competitor accounts to simulate the competitor's pricing strategy, promotion efforts, and live-streaming time selection. Users need to adjust their operation plan based on real-time market data, and the system will output detailed data analysis reports (such as traffic sources, conversion paths, and break-even points) to help users identify problems and optimize strategies.

[0031] We have created a dedicated live-streaming training cabin to address the core needs of live-streaming e-commerce, enhancing users' live-streaming skills through virtual scenarios, AI collaboration, and real-time feedback.

[0032] In terms of virtual scene setup, users can quickly create live streaming scenes (such as beauty live streaming rooms, clothing styling rooms, and food tasting rooms) using templates, supporting customizable backgrounds, lighting, and prop layouts. It also provides a virtual anchor collaboration function, allowing users to choose virtual anchors of different styles (sweet, professional, and funny) and control the virtual anchor's speech and interaction through voice commands or text input, simulating the collaboration mode between real anchors and virtual assistants, reducing the difficulty of operating a single live stream.

[0033] In terms of AI audience and interaction simulation, the system generates virtual audience groups with different behavioral characteristics based on NLP and sentiment analysis technology. Some audiences actively interact (asking questions about product efficacy and price discounts), some have doubts (concerns about quality and after-sales service), some are potential customers (watching silently and occasionally liking), and some leave malicious comments (spaming and defaming products). Users need to respond in real time, and the system will provide real-time scores and optimization suggestions based on the user's response speed, professionalism of their words, and ability to control the situation (e.g., if the words are too stiff, add more contextual descriptions; failure to respond to after-sales questions in a timely manner leads to an increase in audience churn).

[0034] In terms of real-time data feedback and optimization, the training cabin has a built-in live streaming data analysis module that monitors key indicators (number of viewers, interaction rate, conversion rate, average dwell time) in real time and presents them through visual charts. Simultaneously, it incorporates an AI-powered script analysis tool to semantically analyze users' product presentation scripts, identifying strengths (such as prominent selling points and clear logic) and weaknesses (such as a lack of pain point identification and vague descriptions of discounts), and automatically generating optimized script templates for users to reference. Users can practice repeatedly until they reach the preset competency standards.

[0035] To address the disconnect between virtual simulation and real-world operations, we collaborate with mainstream e-commerce platforms and high-quality merchants to build a real-world training zone, providing users with low-cost, low-barrier-to-entry opportunities for real-world operations.

[0036] The technology is integrated with the ecosystem through API interfaces to achieve data interoperability with the e-commerce platform's training channel. After completing designated stages of learning and assessment in the virtual sandbox and live-streaming training cabin, users can apply to enter the real training zone and obtain a dedicated training account. This account has basic operational permissions and can operate some functions of a real store (such as product listing, simple promotion, and customer service), but is subject to dual control by the platform and the ecosystem (such as limiting the categories of products that can be listed, setting a promotion budget cap, and prohibiting violations) to ensure that the training process is compliant and controllable.

[0037] In terms of resource support, partner merchants provide training materials (supporting dropshipping with no inventory pressure), e-commerce platforms offer small-scale traffic support (such as exclusive recommendation slots for training and low-threshold coupons), and the ecosystem provides full guidance (AI customer service answers operational questions in real time, and industry mentors provide regular feedback). Users can earn a certain percentage of commission from real orders generated during the training, achieving a virtuous cycle of learning, practicing, and earning money simultaneously. At the same time, users' training data (such as order volume, conversion rate, and user reviews) will be synchronized to their personal credit profile within the ecosystem, serving as an important basis for future job applications or entrepreneurial resource matching.

[0038] For startup users, the ecosystem integrates core resources such as supply chain, funding, compliance, and operations, and uses technology to achieve precise matching and efficient connection of resources, thereby reducing the threshold and risks of starting a business.

[0039] The supply chain matching module utilizes blockchain and AI matching technologies to build a trustworthy supply chain ecosystem. Merchants joining the system must use blockchain to verify their product qualifications (business license, quality inspection report, traceability information) to ensure product quality and compliance. The AI ​​matching algorithm recommends suitable supply chain resources based on the entrepreneur's target market, training data, and financial strength (e.g., novice entrepreneurs are prioritized for low-minimum-order, high-cost-performance dropshipping suppliers, while experienced entrepreneurs are recommended for customized manufacturing). Simultaneously, the system provides supply chain management tools, supporting automatic order synchronization, real-time logistics tracking, and inventory alerts to help entrepreneurs manage their supply chains efficiently.

[0040] The compliance and tools integration module integrates various compliance tools and services needed for e-commerce startups. Through API interfaces, it enables one-click access to tools such as tax filing (connecting to the tax system, automatically calculating taxes and generating tax returns), intellectual property tools (providing trademark registration search and infringement risk detection), legal consulting services (integrating professional lawyer resources, providing contract review and dispute resolution consultation), and store operation tools (automated customer service responses, data analysis reports, and marketing campaign templates). All tools are provided in tiers according to the startup stage. Basic functions are offered free of charge to beginners, and paid functions can be upgraded after the business becomes profitable, reducing initial cost pressure.

[0041] The funding channel adopts a "credit assessment + intelligent matching" model. The ecosystem collaborates with banks, venture capital institutions, and e-commerce platform financial departments to offer specialized funding support for e-commerce entrepreneurs (micro-entrepreneurial loans, supply chain finance, and angel investment matching). The AI ​​credit assessment model generates a credit score and funding needs matching report based on the entrepreneur's training data, personal credit history, and business plan (AI-assisted generation and optimization), recommending suitable funding channels. Entrepreneurs with good credit and excellent training data can enjoy lower loan interest rates, higher loan amounts, or priority investment opportunities. Simultaneously, the system has a built-in entrepreneurial risk warning module that monitors the startup project's cash flow, order volume, and market competition in real time. If potential risks are detected (such as a broken cash flow or competitor impact), timely warnings are issued and optimization suggestions are provided.

[0042] The startup incubation and community module builds a community for entrepreneurs to exchange ideas, supports team formation by track, region, and startup stage, and promotes experience sharing and resource sharing; regularly holds online startup salons and project roadshows, inviting successful entrepreneurs and investors to share their experiences; provides startup project tracking services, and industry mentors regularly view entrepreneurs' operational data through the system and provide customized optimization suggestions (such as product iteration direction and marketing strategy adjustments).

[0043] For job seekers, the ecosystem builds a full-chain employment service system that includes skills certification, company matching, interview empowerment, and onboarding adaptation. Through technological means, it achieves precise matching between job seekers and companies, improving employment efficiency and quality.

[0044] The core technological highlight is the two-way matching engine between enterprises and job seekers. When enterprises join, they need to fill in detailed job requirements (skills, work experience, job responsibilities, salary and benefits). The system uses NLP technology to analyze the core job requirements and build a job profile for the enterprise. After job seekers complete their learning and training, the system automatically generates a skills certification report (including learning outcomes, training data, skills scores, and suitable job directions) to build a job seeker's competency profile. The matching algorithm adopts a three-dimensional model of "hard skills matching + soft skills adaptation + development potential assessment". Hard skills matching focuses on the core skills required for the job (such as the ability to promote through e-commerce platforms and data analysis skills). Soft skills adaptation focuses on communication skills, stress resistance, teamwork, etc. Development potential assessment is based on learning ability, training performance, and industry adaptability. The system recommends the job seekers with the highest overall match to enterprises and suitable positions to job seekers, and provides a match analysis report (such as "Your data analysis skills meet the job requirements, but you lack cross-border e-commerce experience. It is recommended to supplement relevant learning modules").

[0045] The interview empowerment module employs an AI-powered mock interview + personalized guidance model. Based on a real corporate interview question bank, it generates targeted mock interview scenarios (such as case analysis questions for operations positions and live streaming trial broadcasts for live streaming positions). The AI ​​interviewer interacts with job seekers via voice and text, recording their interview performance in real time, analyzing their language expression, logical thinking, and professional knowledge mastery, and generating an interview evaluation report and optimization suggestions. It also provides interview script templates and resume optimization tools (automatically optimizing resume keywords based on job requirements) to help job seekers improve their interview success rate.

[0046] The onboarding module connects the ecosystem with the enterprise's training system. After job seekers join the company, the system pushes targeted supplementary learning content based on the enterprise's job training needs to help job seekers quickly adapt to their positions. At the same time, it tracks the job seekers' onboarding performance and provides feedback to the ecosystem to continuously optimize learning and training content, forming a closed loop of "employment feedback and content optimization".

[0047] To address the credibility issues of learning outcomes, practical data, and entrepreneurial / employment performance records, blockchain technology is introduced to build a trusted authentication system. Users' learning progress, assessment scores, training data, and skills certification reports are stored on the blockchain, generating unique and tamper-proof digital credentials. Entrepreneurs' supply chain cooperation records, financial performance, and user reviews, as well as job seekers' interview performance and post-employment feedback, are also simultaneously stored on the blockchain.

[0048] This system provides credible evidence for all parties in the ecosystem. When recruiting, companies can verify the authenticity of job seekers' skill certifications and training data through blockchain, preventing resume fraud. In supply chain collaborations, merchants can view entrepreneurs' credit records and contract performance, reducing cooperation risks. When investors connect with startup projects, they can evaluate project value through blockchain-verified operational data, improving investment decision-making efficiency. Simultaneously, users can independently control the sharing permissions of digital credentials, protecting their personal privacy.

[0049] In terms of data security, a triple protection system of "encrypted storage + access control + privacy computing" is adopted. Sensitive information such as user personal information, learning data, and training data are stored using AES and 256 encryption, and the transmission process uses HTTPS protocol and SSL encryption; fine-grained access control policies are established, and different roles (users, enterprises, supply chain merchants, platform administrators) can only access data within their authorized scope; federated learning and differential privacy technologies are used to perform data modeling and analysis without disclosing the original data, thus ensuring data privacy.

[0050] In terms of content security, an AI content review system has been built to monitor in violation of regulations (such as false advertising, illegal and non-compliant content, and malicious attacks) in course content, live training, and community communication in real time. It quickly intercepts and processes violations through text recognition, image recognition, and voice recognition technologies. A content reporting mechanism has also been established, allowing users to report violations. The platform will review the reports manually and process them promptly to ensure the compliance of the ecosystem's content.

[0051] In terms of transaction security, blockchain smart contract technology is introduced to automatically execute transaction agreements (such as automatic payment after the supplier delivers the goods on time, and automatic return of the deposit after the entrepreneur reaches the agreed sales volume) for supply chain transactions and fund transfers of entrepreneurial users, avoiding transaction disputes; a transaction guarantee mechanism is established, in which the platform guarantees the transactions between supply chain merchants and entrepreneurs. If product quality problems or breaches of contract occur, the platform will intervene to coordinate and handle the matter, protecting the rights and interests of both parties.

[0052] The user incentive system adopts a "points + certification + resource allocation" model. Users can earn points by completing learning tasks, participating in practical training, sharing high-quality content, and recommending new users to join. These points can be redeemed for resources such as courses, tool usage rights, and traffic support. Passing skills certification and practical training assessments will earn users corresponding levels of digital certificates. Higher levels will enjoy greater priority in resource matching (such as priority access to high-quality enterprises and supply chains). Profit data from entrepreneurial users and onboarding feedback from employed users can serve as additional incentives, providing more traffic support and resource allocation.

[0053] In terms of a win-win ecosystem partnership model, e-commerce platforms gain access to high-quality merchants and potential employees through the ecosystem, enhancing platform activity and operational efficiency; businesses quickly recruit skilled employees, reducing recruitment costs; supply chain merchants gain stable sales channels, expanding market share; and users receive end-to-end support for learning, practice, entrepreneurship, and employment, achieving personal value enhancement. The platform generates revenue by charging service fees to businesses and supply chain merchants and providing value-added services (such as advanced tools and dedicated mentorship) to users. Simultaneously, a portion of the profits is reinvested in the ecosystem for technology iteration, content updates, and resource expansion, forming a virtuous cycle of sustainable operation.

[0054] The technology iteration and upgrade mechanism establishes a closed loop of user feedback, data monitoring, and iterative optimization. The system monitors user behavior data, function usage frequency, and satisfaction scores in real time to analyze user pain points and functional shortcomings; it regularly collects user feedback and suggestions, and, in conjunction with industry technology development trends (such as new applications of AI big data models and new functions of e-commerce platforms), formulates technology iteration plans, conducts minor version updates every quarter, and major version upgrades every year to ensure the ecosystem's technological leadership and functional compatibility.

[0055] Unlike traditional e-commerce learning platforms that rely on a single AI application, this solution achieves end-to-end integration of multimodal AI: generative AI for customized learning content, business plan generation, and live-streaming script optimization; NLP technology for user needs diagnosis, job profile building, and content review; computer vision technology for virtual scene rendering and interactive live-streaming training; and reinforcement learning for dynamic learning path optimization and virtual market simulation. This integrated application of multimodal AI enables the ecosystem to accurately understand user needs, provide personalized services, and simulate real-world market environments, thereby enhancing user experience and empowerment.

[0056] This innovative approach proposes a progressive practical training path encompassing virtual simulation, semi-realistic training, and real-world operations, addressing the pain point of traditional learning platforms where learning and application are disconnected. The virtual sandbox helps users master basic operational processes, while the live-streamed training cabin enhances specialized skills. The real-world training zone facilitates the transition from simulation to real-world practice, allowing users to accumulate real-world operational experience with zero risk and low cost, significantly improving their entrepreneurial success rate and employment competitiveness.

[0057] By leveraging blockchain technology to provide trusted evidence of learning outcomes, practical data, and transaction records, the trust issues of all parties in the ecosystem are addressed. Businesses no longer need to worry about job seekers falsifying skills; entrepreneurs no longer need to worry about supply chain merchants defaulting on payments; and supply chain merchants no longer need to worry about entrepreneurs delaying payments. This builds a trustworthy, transparent, and efficient ecosystem, enhancing its cohesion and competitiveness.

[0058] Every aspect of the ecosystem is driven by data. From user profiling and learning content recommendation to practical scenario simulation, and even startup resource matching and employment placement, everything is dynamically optimized based on real-time data. This data-driven approach allows the ecosystem to respond quickly to market changes and user needs, continuously improving service quality and empowerment effectiveness to create a differentiated competitive advantage.

[0059] The phased implementation plan will be carried out in two phases. The first phase (1 to 6 months) will involve building the core technical architecture, developing the basic version of the intelligent learning engine and virtual e-commerce sandbox, connecting with 1 or 2 mainstream e-commerce platforms and 50+ supply chain merchants to develop 100+ micro-module courses, recruiting 1000+ seed users for testing, collecting feedback, and optimizing product functions.

[0060] The second phase (7-12 months) involves perfecting all the functions of the virtual e-commerce sandbox and live-streaming training cabin, launching a real training zone, connecting with 3-5 e-commerce platforms, 200+ supply chain merchants and 100+ enterprises to expand the course library to 500+ modules, and optimizing the AI ​​matching algorithm and blockchain authentication system to achieve a user scale exceeding 100,000.

[0061] The third phase (13 and 24 months) will involve the full launch of the entrepreneurship empowerment system and employment matching system, expanding into more sectors such as cross-border e-commerce and social e-commerce, connecting with 10+ e-commerce platforms, 500+ supply chain merchants, and 500+ enterprises to establish a comprehensive ecosystem partnership system, with the user base exceeding 500,000 and achieving a profit loop.

[0062] The fourth phase (25 months or more) involves continuous technological iteration and functional optimization to expand the ecosystem's boundaries (such as integrating more e-commerce-related services like logistics, finance, and legal services), creating an industry-leading ecosystem platform with a user base exceeding 1 million, forming a nationwide e-commerce learning, entrepreneurship, and employment ecosystem network.

[0063] The technical risk lies in the high difficulty of developing core technologies, which may lead to system stability issues. The countermeasures include assembling a professional technical R&D team to tackle technical challenges module by module, introducing third-party technical consulting services, and establishing a comprehensive testing system to conduct thorough stress testing and compatibility testing to ensure stable system operation.

[0064] The market risk lies in the rapid changes in the e-commerce industry, which may lead to fluctuations in market demand and a disconnect between ecosystem services and market needs. The countermeasures include establishing a real-time market monitoring mechanism, connecting industry data tools and e-commerce platform interfaces to quickly respond to market changes; and maintaining rapid iteration of course content and functions to adjust the focus of ecosystem services according to market demands.

[0065] E-commerce platforms, businesses, and supply chain merchants may default on contracts or terminate cooperation, impacting ecosystem operations. Countermeasures include signing detailed cooperation agreements to clarify the rights and obligations of both parties; establishing multi-channel cooperation mechanisms to avoid over-reliance on a single partner; and using blockchain smart contracts to ensure contract fulfillment and reduce cooperation risks.

[0066] Initial user acquisition may be challenging, leading to slow user growth. To address this, targeted marketing strategies can be implemented, focusing on specific user groups such as e-commerce professionals, the unemployed, and university students. Invitations to refer new users through referral programs can be incentivized, along with collaborations with universities and vocational training institutions. Furthermore, the platform can be integrated into e-commerce-related professional training courses to expand user acquisition.

[0067] The embodiments of this application have been described above with reference to the accompanying drawings. Unless otherwise specified, the embodiments and features in the embodiments of this application can be combined with each other. This application is not limited to the specific embodiments described above. The specific embodiments described above are merely illustrative and not restrictive. Those skilled in the art can make many other forms under the guidance of this application without departing from the spirit and scope of the claims, and all of these forms are within the protection scope of this application.

Claims

1. An e-commerce industry online learning and entrepreneurship employment ecological method, characterized in that: Comprise the following steps: Step one: Construct user portrait through mixed mode of AI evaluation and manual review, and portrait data is processed by federal learning to ensure privacy security; Step two: Based on user portrait and market real-time demand, generate dynamic optimized learning path through mixed recommendation algorithm of fusion collaborative filtering, content recommendation and demand matching combined with reinforcement learning mechanism; Step three: Provide learning content of micro module, scene and generative design, realize content real-time update through three-party linkage mechanism, and form learning, feedback and optimization closed loop; Step four: Build virtual e-commerce sand table and live training cabin to provide zero-cost virtual combat scene, support multi-e-commerce mode full-process simulation and special skill intensive training; Step five: Connect mainstream e-commerce platform and high-quality merchants to open real training area for users to provide compliant and controllable real operation opportunity and commission sharing; Step six: Integrate supply chain, funds, compliance and other resources to provide precise matching and efficient docking services for entrepreneurial users; Build full-link employment service system to realize precise matching of job seekers and enterprises; Step seven: Introduce blockchain technology to build credible authentication system to store evidence of user learning achievements, practice data and other information; Adopt triple protection to ensure data, content and transaction security; Step eight: Through the incentive system of points, authentication and resource tilt, realize the win-win mode of ecological partners to achieve sustainable operation of ecology.

2. The online learning and entrepreneurship employment ecological method and system of the e-commerce industry according to claim 1, characterized in that: The multi-modal data in step one includes four categories of basic attributes, career goals, ability dimensions and behavior characteristics; The AI evaluation adopts self-adaptive question bank to dynamically adjust the difficulty of questions, combines natural language processing to analyze open-ended answers, and outputs ability short board report; For users with fuzzy goals, interactive questionnaire and scene simulation are used to generate career adaptability analysis. 3.The online learning and entrepreneurship employment ecological method and system of e-commerce industry according to claim 1, characterized in that: The learning path optimization in step two is adjusted in real time according to user learning progress, evaluation results and practice feedback; Core courses are preferentially pushed to users with different goals according to their ability short board, and after the basic skills are up to standard, the users are automatically connected to advanced content.

4. The online learning and entrepreneurship employment ecological method and system of the e-commerce industry according to claim 1, characterized in that: The content update mechanism of three-party linkage in step three includes: connecting mainstream e-commerce platform to open interface to grab new rules information, AI to automatically generate interpretation courses; Access industry data analysis tools to obtain real-time data and update related courses synchronously; Industry experts output in-depth insights, and AI assists in making multi-form courses.

5. The online learning and entrepreneurship employment ecological method and system of the e-commerce industry according to claim 1, characterized in that: The virtual e-commerce sand table in step four adopts WebGL combined with Unity3D front-end rendering scheme and micro-service architecture back-end design, supports full-process simulation of four scenes of traditional shelf e-commerce, live e-commerce, social e-commerce and cross-border e-commerce, and has built-in different difficulty levels; Through Monte Carlo simulation algorithm, dynamic market environment is generated, virtual audience and competitor behavior are simulated, and data analysis report is output.

6. The online learning and entrepreneurship employment ecological method and system of the e-commerce industry according to claim 1, characterized in that: The live training cabin in step four supports custom virtual scene building and virtual anchor cooperation; Based on NLP and sentiment analysis technology, generate multi-feature virtual audience group, simulate interaction and unexpected situations, and give real-time score and optimization suggestions according to user performance; Built-in data analysis module and AI dialogue analysis tool provide data visualization and dialogue optimization template.

7. The online learning and entrepreneurship employment ecological method and system of the e-commerce industry according to claim 1, characterized in that: The real training area in step five is connected with the e-commerce platform training channel data through API interface; users obtain exclusive training accounts after completing the specified stage learning assessment, have basic operation rights and are subject to double control; cooperative merchants provide one piece of goods for delivery, e-commerce platforms provide small amount of traffic support, ecology provides whole process guidance, and training data is synchronized to personal credit files. 8.The online learning and entrepreneurship employment ecological method and system of e-commerce industry according to claim 1, characterized in that: The entrepreneurship resource docking in step six includes: the supply chain docking module uses blockchain storage product qualification and AI matching technology to recommend adaptive resources, and provides supply chain management tools; the compliance and tool integration module connects various compliance tools and services through API interface, and provides them according to the entrepreneurship stage; the fund docking channel uses credit evaluation and intelligent matching to provide special fund support and risk warning. 9.The e-commerce industry online learning and entrepreneurship employment ecological method and system of claim 1, characterized in that: The employment service system in step six includes constructing a job portrait by analyzing enterprise job requirements through NLP technology, and constructing a capability portrait by generating a job seeker skill certification report; The matching algorithm uses a three-dimensional model of hard skill matching, soft quality adaptation and development potential evaluation; AI simulated interviews, resume optimization tools and onboarding adaptive supplementary learning content are provided.

10. The online learning and entrepreneurship employment ecological method and system of the e-commerce industry according to claim 1, characterized in that: The triple protection in step seven includes that sensitive information is stored through AES-256 encryption, transmission uses HTTPS and SSL encryption; a fine-grained access control strategy is established; federal learning and differential privacy technology are used to protect data privacy; and the trusted authentication system supports user self-control of digital certificate sharing rights.

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