A method and system for matching questions and answers in innovation and entrepreneurship guidance

Abstract

This invention relates to the field of data processing technology, and in particular to a method and system for matching questions and answers in innovation and entrepreneurship coaching. It includes constructing and analyzing dynamic user profiles based on user data, and combining current coaching questions with the dynamic user profiles to classify questions and deduce the user's true intentions. Compared to existing technologies that typically rely solely on direct user questions or simple interaction history to understand needs, failing to reflect the user's true capabilities and the root causes of problems, this invention systematically integrates multi-source information such as user-uploaded project documents and historical behavioral data. Through attribution analysis and capability assessment models, it dynamically constructs a structured profile containing capability maps, core challenges, and their root causes. This approach enables the system to go beyond surface-level issues, deeply understanding the user's true situation and inherent weaknesses, laying a solid cognitive foundation for providing highly personalized coaching.

Description

Technical Field

[0001] This invention relates to the field of data processing technology, and in particular to a method and system for matching questions and answers in innovation and entrepreneurship guidance. Background Technology

[0002] In the field of innovation and entrepreneurship mentoring, traditional online Q&A or knowledge base systems aim to provide entrepreneurs with immediate information support and decision-making references. These systems typically rely on keyword matching, FAQ retrieval, or document-based question-answering techniques, attempting to associate user-submitted questions with pre-stored knowledge or case studies. With the development of artificial intelligence, some systems have begun to introduce basic personalized recommendation elements, such as adjusting the order of information presentation based on user tags or browsing history. However, the core paradigm of these methods still views mentoring as a one-way process of information retrieval and distribution, and its effectiveness is largely limited by the system's depth of understanding of the user's complex situations and deep-seated needs.

[0003] Existing technical solutions have several significant drawbacks. First, they generally lack dynamic and comprehensive modeling of the individual entrepreneur's situation, often responding only based on a single question or superficial interaction data. This fails to understand the root causes of the user's predicament, their own capability limitations, and specific resource constraints, resulting in superficial and generic answers. Second, at the problem understanding level, most systems can only process the surface-level demands directly expressed by users, struggling to discern the true intentions and potential concerns unspoken due to cognitive limitations, leading to insufficiently targeted reference information. Third, in terms of coaching methods, existing technologies mostly involve passive responses or static case demonstrations. Users cannot practice decision-making in a simulated environment and observe the consequences in real time, resulting in a poor immersive learning experience and difficulty in transforming knowledge into practical decision-making abilities. Finally, the entire coaching process is typically linear and open-loop, lacking comparative analysis and iterative optimization guidance based on user decision results, limiting the effectiveness of deep learning from experience.

[0004] This invention aims to solve the aforementioned problems by proposing an innovation and entrepreneurship coaching question-and-answer matching method and system. First, the invention constructs a dynamic profile of users, including a capability map and attribution of difficulties, by analyzing multi-source user data to gain a deep understanding of the user's background. Then, it combines the user's original questions with this profile to deduce deeper, constrained, and genuine intentions. Subsequently, the system not only retrieves similar cases but also guides users to conduct multiple rounds of interactive decision-making in simulated scenarios by creating virtual roles and a business rule engine, and extrapolates the impact in real time. Finally, the system compares and analyzes the user's simulated results with real cases, allowing for retrospective optimization, thereby completing a full coaching cycle from cognition and practice to reflection and improvement. Summary of the Invention

[0005] To overcome the problems mentioned in the background art, the present invention proposes an innovation and entrepreneurship guidance question-and-answer matching method and system.

[0006] The technical solution of this invention is: a question-and-answer matching method for innovation and entrepreneurship guidance, comprising the following steps:

[0007] S11: Receive the current tutoring question and related user data input by the user;

[0008] S12: Based on user data, construct and analyze dynamic user profiles, which include user capability graphs, current difficulties, and attribution analysis of difficulties;

[0009] S13: Combining the current tutoring questions with the dynamic user profile, classify the questions and deduce the user's true intentions to generate a more in-depth question with context and constraints;

[0010] S14: Based on the type and characteristics of the in-depth questions, retrieve and extract similar enterprise cases from the pre-built enterprise case knowledge base;

[0011] S15: Extract similar enterprise cases, integrate expert evaluations and related network materials, and automatically summarize and categorize them to generate structured case summaries;

[0012] S16: Based on the structured case summary and in-depth questions, initiate the guided question-and-answer process;

[0013] S17: Compare and analyze the simulation results with the real-world results in the structured case summary, and guide users to optimize their decisions and responses based on this, generating the final coaching conclusions and optimization suggestions.

[0014] User data includes text, forms, and presentation documents uploaded by users, as well as historical Q&A records and behavioral data generated during interactions.

[0015] As a preferred option, when constructing and analyzing dynamic user profiles based on user data, the specific steps include:

[0016] S21: Receive and preprocess various types of input data from the user;

[0017] S22: Based on the preprocessed data, identify and define the core dilemmas currently faced by the user;

[0018] S23: Conduct attribution analysis on the core dilemmas to identify the root causes of each dilemma and related secondary issues;

[0019] S24: Based on the input data and attribution analysis results, construct and update the user's capability map in each preset business area;

[0020] S25: Integrate core dilemmas, root causes, secondary problems, and capability maps to form a structured dynamic user profile, and output it for subsequent intent understanding and case matching.

[0021] As a preferred approach, when combining current tutoring questions with dynamic user profiles to categorize questions and deduce users' true intentions, and generating a more in-depth question with context and constraints, the specific steps include:

[0022] S31: Receive the current tutoring question text input by the user and obtain the constructed dynamic user profile;

[0023] S32: Automatically categorize the current tutoring question text to determine its question category and related business area;

[0024] S33: Combining dynamic user profiles, conduct in-depth analysis of the current coaching questions that have been categorized to deduce the user's true intentions;

[0025] S34: Integrate the question category, relevant business areas, the user's true intent, and specific constraints extracted from the dynamic user profile to generate a structured, in-depth question containing a complete decision-making context;

[0026] S35: Output in-depth questions for use in subsequent case matching and simulated coaching processes.

[0027] As a preferred approach, deriving the user's true intent specifically includes:

[0028] S331: Analyze dynamic user profiles to extract users' skill gaps, current core difficulties, and their root causes;

[0029] S332: Map and analyze the contradictions between the surface demands of the current coaching problem and the extracted skill gaps, current core dilemmas, and root causes;

[0030] S333: Based on association mapping and contradiction analysis, identify users' unstated needs, potential concerns and decision priorities. Unstated needs include the need for solutions that users have not been aware of due to their own limitations. Potential concerns include worries about the root causes of the predicament.

[0031] S334: Generate a textual description of the user's true intent by integrating surface-level appeals, unstated needs, and potential concerns.

[0032] As a preferred method, when retrieving and extracting similar enterprise cases from a pre-built enterprise case knowledge base based on the type and characteristics of the in-depth questions, the specific steps include:

[0033] S41: Receive the in-depth question generated by the intent deduction module. The in-depth question includes the question category, main business domain, related dilemmas, true intent and specific constraints.

[0034] S42: Analyze and deepen the problem, and extract multi-dimensional retrieval feature vectors for case retrieval;

[0035] S43: Based on multi-dimensional retrieval feature vectors, perform multi-level retrieval and similarity calculation in a pre-built enterprise case knowledge base;

[0036] S44: Based on the similarity calculation results, optimize the sorting and filtering of the retrieved candidate cases;

[0037] S45: Extract at least one most similar business case from the sorted results and output its complete or summary information to the subsequent summary module.

[0038] Preferably, each case in the pre-built enterprise case knowledge base is stored in a structured format and includes at least the following index fields: case ID, enterprise background description, industry, development stage, core problem description, action description, final result, and pre-computed case semantic vector; wherein, multi-level retrieval and similarity calculation specifically include:

[0039] S431: Preliminary screening uses structured search keys in multi-dimensional search feature vectors to perform precise or fuzzy matching in the knowledge base, quickly filtering out a preliminary set of candidate cases that are basically matched in dimensions including problem category, business domain, industry, and development stage.

[0040] S432: Refined calculation: For each case in the preliminary candidate case set, calculate the cosine similarity between its case semantic vector and the semantic feature vector in the multi-dimensional retrieval feature vector to obtain a semantic similarity score.

[0041] S433: Comprehensive score, which combines semantic similarity score and weighted score based on structured tag matching degree, and generates a comprehensive similarity score for each candidate case through weighted calculation.

[0042] As a preferred approach, when automatically summarizing and generating structured case summaries from extracted similar enterprise cases, integrating expert evaluations and related online materials, the specific steps include:

[0043] S51: Receive identification information for similar enterprise cases;

[0044] S52: Based on the identification information, obtain the basic case data of the corresponding enterprise case, and at the same time obtain the expert evaluation data and related online materials associated with the case;

[0045] S53: Extract key information elements related to the case from basic case data, expert evaluation data, and related online materials respectively;

[0046] S54: Integrate, align, and resolve conflicts among key information elements extracted from different sources, and then conduct inductive analysis to extract core insights.

[0047] S55: Based on the preset structured summary template, integrate and fill in the key information elements and core insights of the basic case data to generate a structured case summary;

[0048] S56: Output a structured case summary for subsequent guided question answering and result comparison.

[0049] The identification information includes at least the unique ID of the case, as well as the core issues of the case, the industry to which it belongs, and keywords related to the company name.

[0050] As a preferred approach, the guided question-and-answer process includes:

[0051] S61: Based on the decision-making scenarios and business domains involved in the in-depth problem, instantiate multiple predefined virtual roles, and configure each role with a stance, knowledge background and dialogue objectives that match the scenario and domain;

[0052] S62: Construct a simulated scenario based on in-depth questions and structured case summaries, and guide the user to make specific decisions and answers for preset phased goals in the simulated scenario through multiple rounds of interaction with multiple virtual characters.

[0053] S63: Based on a preset business rule model, it performs real-time impact analysis on the decisions or answers made by users in each round of interaction, updates the state of the simulated scenario, and generates the final simulation result based on the cumulative decision sequence of multiple rounds.

[0054] S64: Compare the simulation results with the real enterprise results recorded in the structured case summary and output them to guide users to make decision optimizations based on the comparison.

[0055] As a preferred approach, when comparing and analyzing simulation results with real-world results in structured case summaries, and guiding users to optimize their decisions and responses based on this comparison to generate final coaching conclusions and optimization suggestions, the specific steps include:

[0056] S71: Compare and analyze the simulation results under the user's decision-making path with the actual results corresponding to the real enterprise action paths recorded in the structured case summary from multiple dimensions, and generate a difference analysis report;

[0057] S72: Based on the difference analysis report and the core insights in the structured case summary, generate attribution explanations and optimization suggestions for the user's decision-making path;

[0058] S73: Initiate an interactive decision optimization guidance process, allowing users to reselect at key decision points and observe new simulation results;

[0059] S74: Based on the final simulation path and optimization choice, and drawing inspiration from the integrated structured case summary, generate a structured final coaching conclusion and conclude this round of coaching sessions.

[0060] An innovation and entrepreneurship mentoring question-and-answer matching system includes:

[0061] The user interface module is used to receive user input data and questions;

[0062] The user profile building and analysis module is used to build and analyze dynamic user profiles based on user data;

[0063] The intent understanding and question refinement module is used to generate refined questions based on user questions and dynamic user profiles.

[0064] The case retrieval and matching module is used to retrieve similar cases from the enterprise case knowledge base based on in-depth questions;

[0065] The multi-source information fusion and summarization module is used to fuse multi-source information on similar cases and generate structured case summaries;

[0066] A guided simulation coaching engine is used to guide users in decision-making simulations and generate simulation results through virtual roles, based on in-depth questions and structured case summaries.

[0067] The comparative analysis and optimization generation module is used to compare and analyze the simulation results with the results in the structured case summary, and generate coaching conclusions and optimization suggestions.

[0068] The beneficial effects of this invention are:

[0069] 1. Compared to existing technologies that typically rely solely on users' direct questions or simple interaction history to understand needs, this approach captures a static and one-sided user state that fails to reflect the user's true capabilities and the root causes of problems. This invention employs a deeply integrated analysis scheme that systematically integrates multi-source information such as user-uploaded project documents and historical behavioral data. Through attribution analysis and capability assessment models, it dynamically constructs a structured profile that includes capability maps, core challenges, and their root causes. This scheme enables the system to go beyond surface-level issues and deeply understand the user's true situation and inherent weaknesses, laying a solid cognitive foundation for providing highly personalized guidance in the future.

[0070] 2. Compared to existing technologies that mostly focus on semantic classification or keyword matching of user questions, which tend to remain at the literal meaning of the question and fail to address the deeper concerns that users may not have explicitly expressed due to knowledge limitations, this invention innovatively maps and analyzes the contradictions between the current problem and the user's skill gaps and predicament attributions in the user profile. This proactively deduces the user's unspoken needs and true intentions, and integrates specific constraints to generate a more in-depth question with a complete decision-making context. This approach transforms the original, potentially vague question into a clearly defined and richly meaningful guidance input, greatly improving the accuracy and relevance of subsequent steps.

[0071] 3. Compared to traditional question-and-answer or case-based teaching, which often focuses on one-way information transmission and places users in a passive receiving state, lacking the experience of actively making decisions in complex situations and obtaining immediate feedback, this invention creatively constructs a simulated business scenario, instantiating multiple virtual characters with different positions and goals. Through multi-round dialogues, it guides users to make a series of decisions and uses a built-in business rule model to deduce the impact of decisions on key business indicators in real time. This approach places users in an immersive, dynamically changing decision-making training sandbox, enabling them to practice and observe the consequences of decisions in a risk-free environment, thereby achieving a fundamental shift from knowledge transmission to skills training.

[0072] 4. Compared to existing technologies that typically present retrieved cases as a whole without in-depth analysis or structured extraction, requiring users to filter and interpret them themselves, resulting in low learning efficiency, this invention employs a multi-source information fusion strategy. It not only retrieves basic cases but also integrates expert analysis and online materials. Through information alignment and conflict resolution, it ultimately generates a structured summary containing core concepts, multi-dimensional analysis, and transferable insights. This approach transforms scattered information into decision-making references with clear viewpoints and direct relevance. In particular, the "transferable insights" section directly connects historical experience with the user's current predicament, significantly enhancing the inspirational value and practical effectiveness of case study learning.

[0073] 5. Compared to existing tutoring or simulation systems that often end after providing results or suggestions, lacking a closed-loop process that prompts users to reflect on and verify different choices, this invention, after users complete the simulation, conducts a multi-dimensional comparative analysis of their decision-making path and results with real-world cases, generates attribution explanations, and allows users to go back to any key decision point and make a new choice to observe the branching results brought about by different decisions. This design creates a complete learning cycle of "decision-feedback-reflection-optimization," guiding users to deeply understand the causal relationships of business logic through comparison, thereby elevating a one-time tutoring experience into a sustainable, cognitively iterative deep learning process. Attached Figure Description

[0074] Figure 1 The diagram shown is a flowchart of the innovation and entrepreneurship guidance question-and-answer matching method of the present invention.

[0075] Figure 2 The diagram shown is a schematic representation of the structure of the innovation and entrepreneurship guidance question-and-answer matching system of the present invention. Detailed Implementation

[0076] The present invention will be further described below with reference to the accompanying drawings and embodiments.

[0077] Please see Figure 1 This invention provides an embodiment: a question-and-answer matching method for innovation and entrepreneurship guidance, comprising the following steps:

[0078] S11: Receive the current tutoring question input by the user and related user data, including text, forms, and presentation documents actively uploaded by the user, as well as historical question and answer records and behavioral data generated during the interaction;

[0079] S12: Based on user data, construct and analyze dynamic user profiles. These profiles include the user's capability map, current predicament, and predicament attribution analysis, specifically including:

[0080] S21: Receive and preprocess various types of input data from the user. The specific process is as follows:

[0081] S211: Receive user data from various sources, including project documents, business plans, financial data, market research reports, and historical Q&A texts, selection records, and interaction logs generated within the system that are actively uploaded by users;

[0082] S212: Clean, standardize and correlate user data from multiple sources, and perform natural language processing on unstructured text data to extract key information entities, numerical indicators and declarative facts;

[0083] S213: Store the extracted information into a structured user information database and associate each piece of information with a timestamp and a data source tag;

[0084] S22: Based on the preprocessed data, identify and define the core dilemma currently faced by the user. The specific process is as follows:

[0085] S221: Extract key text snippets and data metrics containing problem descriptions, challenge statements, and negative results from a structured user information database;

[0086] S222: Use natural language processing models to classify key text fragments and identify the types of business issues they reflect;

[0087] S223: Combining the preset dilemma identification rule engine, comprehensively analyze the classified problems, related negative data indicators and their frequency and severity, and locate and define the core dilemma;

[0088] S224: Generate a structured description of each identified core dilemma, including the dilemma name, the business area it belongs to, the relevant evidence, and the severity level;

[0089] S23: Conduct attribution analysis on the core dilemmas to identify the root causes of each dilemma and related secondary issues. The specific process is as follows:

[0090] S231: For each core dilemma, retrieve data from the user information database on events, decisions, performance, and external environment that are relevant to the dilemma in terms of time and business.

[0091] S232: Construct an attribution analysis model based on attribution theory, which logically correlates the retrieved related data with the core dilemma to deduce the root cause of the dilemma.

[0092] S233: Based on the root cause, further deduce other related issues arising from or coexisting with the core dilemma as secondary issues;

[0093] S234: Generate a structured attribution analysis report that clearly lists each core dilemma, its corresponding root cause chain, and related secondary issues;

[0094] S24: Based on the input data and attribution analysis results, construct and update the user's capability map in each preset business area. The specific process is as follows:

[0095] S241: Defines multiple pre-defined business capability dimensions, including areas such as strategic planning, product development, marketing, sales management, operations and production, financial management, and team building;

[0096] S242: Design a set of assessment indicators for each competency dimension, and extract evidence data related to each assessment indicator from the user information database and attribution analysis report, including behavioral descriptions, project outcome data, skill self-assessment, third-party feedback and decision results;

[0097] S243: Use a capability assessment model to quantify the evidence data and calculate the user's current score in each capability dimension;

[0098] S244: Integrate the scores of all ability dimensions and present them in the form of a structured data table to form the user's ability map. The map dynamically reflects the relative strength of the user's abilities in each field.

[0099] S25: Integrate core challenges, root causes, secondary issues, and capability maps to form a structured dynamic user profile, and output it for subsequent intent understanding and case matching. The structured dynamic user profile is a queryable and updatable data object that contains at least the following related fields:

[0100] User ID; timestamp and profile version number; current core challenges list; challenges attribution analysis results; capability graph data; summary of source data on which profile generation is based.

[0101] Specifically, this invention aims to construct and analyze dynamic user profiles through an automated process. First, the system receives and preprocesses various documents and historical interaction data uploaded by users, extracting key information and storing it in a structured database. Then, based on this information, natural language processing and a rule engine are used to identify the core challenges faced by users. Next, an attribution analysis model is used to explore the root causes and related secondary issues of each challenge. Simultaneously, the system extracts evidence from the data and attribution results, quantifying the user's competency scores in multiple preset business areas (such as strategy, finance, and marketing) using a competency assessment model, forming a competency map. Finally, the challenges, attribution results, and competency map are integrated to generate a queryable and updatable structured dynamic user profile containing all the above elements, providing a basis for subsequent precise guidance.

[0102] S13: Combining the current coaching questions with dynamic user profiles, classify the questions and deduce the user's true intentions to generate a more in-depth question with context and constraints, specifically including:

[0103] S31: Receive the current tutoring question text input by the user and obtain the constructed dynamic user profile, wherein the dynamic user profile includes at least the user's capability graph, current core dilemma, dilemma attribution analysis, user's industry and development stage information;

[0104] S32: Automatically categorize the current tutoring question text to determine its question category and related business area. The specific process is as follows:

[0105] S321: Preprocess the current tutoring question text, including word segmentation, stop word removal, and stemming;

[0106] S322: Extract keywords, key phrases, and syntactic structure features from the preprocessed text;

[0107] S323: Input the extracted features into a pre-trained problem classification model, and output the probability distribution of problem categories and related business domains through the model;

[0108] S324: Based on the probability distribution, assign the current coaching question to a preset question category and business area. The question categories include strategic decision-making, operational execution, resource acquisition and risk response, and the business areas include marketing, product, finance, team and legal.

[0109] S33: Combining dynamic user profiles, conduct in-depth analysis of the categorized current coaching questions to deduce the user's true intent. The specific process is as follows:

[0110] S331: Analyze dynamic user profiles to extract users' skill gaps, current core difficulties, and their root causes;

[0111] S332: Map and analyze the contradictions between the surface demands of the current coaching problem and the extracted skill gaps, current core dilemmas, and root causes;

[0112] S333: Based on association mapping and contradiction analysis, identify users' unstated needs, potential concerns and decision priorities. Unstated needs include the need for solutions that users have not been aware of due to their own limitations. Potential concerns include worries about the root causes of the predicament.

[0113] S334: Generate a textual description of the user's true intent by comprehensively considering surface-level appeals, unstated needs, and potential concerns;

[0114] S34: Integrate the question category, relevant business domain, user's true intent, and specific constraints extracted from the dynamic user profile to generate a structured, in-depth question containing a complete decision-making context. The specific process is as follows:

[0115] S341: Extract the specific constraints necessary for generating in-depth questions from the dynamic user profile. The specific constraints include the user company's resource limitations, development stage characteristics, core team capability boundaries, and identified risks.

[0116] S342: Construct a deeper problem template, which includes the following fields: original problem summary, problem category, main business area, related dilemmas, deduced true intent, list of specific constraints, and expected output format;

[0117] S343: Fill the corresponding fields in the in-depth question template with the question category, business domain, true intent, and specific constraints;

[0118] S344: The completed template is formatted into a compound query statement containing clear context, boundaries, and expectations, which is a structured deepening problem;

[0119] S35: Output in-depth questions for use in subsequent case matching and simulated coaching processes.

[0120] As described above, this invention achieves a precise understanding of the transformation from the user's initial question to their deeper needs. First, the system receives the user's current tutoring question and obtains their constructed dynamic user profile (including information such as skill gaps, core difficulties, and their root causes). Through preprocessing, feature extraction, and classification model prediction of the question text, the system automatically identifies the question category (e.g., strategic decision-making, resource acquisition) and business area (e.g., marketing, finance). Based on this, the system correlates and analyzes the surface-level demands of the question with the deeper information in the user profile (e.g., skill gaps, root causes of difficulties), thereby revealing the user's unspoken needs and true concerns. Finally, this information, along with specific constraints extracted from the profile (e.g., resource limitations, capability boundaries), is integrated into a preset template to generate a structured, in-depth question with complete context, clear boundaries, and a clear intent, providing precise input for subsequent accurate case matching and simulated tutoring.

[0121] S14: Based on the type and characteristics of the in-depth questions, retrieve and extract similar enterprise cases from the pre-built enterprise case knowledge base, specifically including:

[0122] S41: Receive the in-depth question generated by the intent deduction module. The in-depth question includes the question category, main business domain, related dilemmas, true intent and specific constraints.

[0123] S42: Analyze and deepen the problem, extract multi-dimensional retrieval feature vectors for case retrieval. The specific process is as follows:

[0124] S421: Extract structured search keys directly from in-depth questions, including explicit question category tags, main business area tags, and industry tags and development stage tags parsed from specific constraints;

[0125] S422: Perform natural language processing on the textual descriptions of true intent and related dilemmas in the deepening problem, and transform them into dense semantic feature vectors through an embedding model;

[0126] S423: Combine structured search keys with semantic feature vectors to form a multi-dimensional search feature vector;

[0127] S43: Based on multi-dimensional retrieval feature vectors, multi-level retrieval and similarity calculation are performed in a pre-built enterprise case knowledge base. Each case in the pre-built enterprise case knowledge base is stored in a structured format and includes at least the following index fields: case ID, enterprise background description, industry, development stage, core problem description, action description, final result, and pre-calculated case semantic vector. The multi-level retrieval and similarity calculation specifically include:

[0128] S431: Preliminary screening uses structured search keys in multi-dimensional search feature vectors to perform precise or fuzzy matching in the knowledge base, quickly filtering out a preliminary set of candidate cases that are basically matched in dimensions including problem category, business domain, industry, and development stage.

[0129] S432: Refined calculation: For each case in the preliminary candidate case set, calculate the cosine similarity between its case semantic vector and the semantic feature vector in the multi-dimensional retrieval feature vector to obtain a semantic similarity score.

[0130] S433: Comprehensive score, which combines semantic similarity score and weighted score based on structured tag matching degree, and generates a comprehensive similarity score for each candidate case through weighted calculation;

[0131] S44: Based on the similarity calculation results, optimize, sort, and filter the retrieved candidate cases. The specific process is as follows:

[0132] S441: Sort all preliminary candidate cases in descending order based on the overall similarity score;

[0133] S442: Optimize the sorted list by applying filtering rules, which include: setting a comprehensive similarity score threshold, limiting the number of cases from the same source or the same company, and prioritizing cases with in-depth expert analysis or complete data.

[0134] S443: Generate an optimized and filtered final list of similar cases, sorted by priority;

[0135] S45: Extract at least one most similar enterprise case from the sorted results and output its complete or summary information to the subsequent summary module. Specifically, select the top N cases from the final list of similar cases as output, where N is an integer greater than or equal to 1. The output case information includes at least its core problem, actions taken, final result, and an index pointing to the complete case data, so that the subsequent module can retrieve detailed information for summary.

[0136] As described above, this invention first receives in-depth questions containing multi-dimensional features and extracts structured labels (such as question category and industry) and deep semantic feature vectors from them. Next, a two-step retrieval is performed in a pre-built enterprise case knowledge base: first, the structured labels are used to quickly filter out a candidate set of basic matches; then, the similarity of semantic vectors is calculated for refined scoring, and the two are combined to generate a comprehensive similarity score. Subsequently, the system sorts the cases according to this score and applies preset rules (such as score threshold and data integrity priority) to further optimize the filtering, generating a final case list ranked by priority. Finally, the system outputs the core information and complete data index of one or more of the highest-ranked most similar cases in the list, providing accurate input for subsequent in-depth analysis and summarization.

[0137] S15: Extracted similar enterprise cases are integrated with expert evaluations and related online materials to automatically summarize and generate structured case summaries, specifically including:

[0138] S51: Receive identification information for similar enterprise cases, wherein the identification information includes at least the unique ID of the case, as well as the core issues of the case, the industry to which it belongs, and keywords of the enterprise name;

[0139] S52: Based on the identification information, obtain the basic case data of the corresponding enterprise case, and simultaneously obtain the expert evaluation data and related online materials associated with the case. The specific process includes:

[0140] S521: Retrieve complete basic case data from the local or remote enterprise case knowledge base based on the case ID, including case background, core issues, key actions taken, and final results;

[0141] S522: Retrieve expert evaluation data from the expert evaluation database that is linked to the case ID or company name keywords. The expert evaluation data includes, but is not limited to, expert commentary text, success / failure factor analysis, and scores of key points that can be learned from.

[0142] S523: By calling the web search interface, using the company name, core issues, and key actions as combined keywords, crawl or retrieve publicly available related web materials, including news reports, industry analysis articles, forum discussions, and academic research citations.

[0143] S53: Extract key information elements related to the case from the basic case data, expert evaluation data, and related online materials. The specific process is as follows:

[0144] S531: Analyze the text of basic case data and extract structured factual information elements according to the framework of background-problem-action-result;

[0145] S532: Perform opinion extraction and sentiment analysis on the text of expert evaluation data, and identify and extract the success factors, lessons learned from failures, key decision points, risk assessments and transferable rules of thumb pointed out by experts.

[0146] S533: Perform information filtering, deduplication, and credibility assessment on the text of related online materials, and extract supplementary details to the basic case data, comments from third-party perspectives, information on long-term subsequent impacts, and relevant industry-wide data or trends.

[0147] S54: Integrate, align, and resolve conflicts among key information elements extracted from different sources, and then conduct inductive analysis to extract core insights. The specific process is as follows:

[0148] S541: Information alignment maps comments, opinions, and supplementary details from expert evaluations and online materials to the corresponding stages or elements of the basic case data, establishing cross-source information relationships.

[0149] S542: Conflict detection and resolution. When information from different sources contradicts each other in its description of the same fact, a decision is made based on a pre-defined source priority rule. The source priority rule stipulates that expert evaluation data usually has a higher priority than related network materials, and the original results in the basic case data have the highest priority.

[0150] S543: Summarize and extract the core insights about the case based on the aligned multi-source information and using natural language generation or summarization techniques. The core insights include a summary of the underlying reasons for success and failure, an analysis of the decision-making logic, and targeted implications for current users.

[0151] S55: Based on the preset structured summary template, integrate and populate the key information elements and core insights of the basic case data to generate a structured case summary. The structured summary template is a preset framework containing fixed fields and logical relationships, and it includes at least the following parts:

[0152] Case identifiers: Case name / ID, industry, development stage;

[0153] Core framework: An overview of the background, problems, actions, and results extracted from basic case data;

[0154] Multi-dimensional analysis: This integrates expert evaluations and insights from online materials to analyze success factors, failure risk points, and key decision moments;

[0155] Transferable insights: A list of actionable lessons and suggestions that directly address the current challenges and competency profiles of the users being coached;

[0156] The specific process of outputting a structured case summary is as follows: the extracted factual information elements are filled into the core context, and the obtained core insights are filled into the multi-dimensional analysis and transferable revelations to generate a structured case summary with integrated information and clear viewpoints.

[0157] S56: Output a structured case summary for subsequent guided question answering and result comparison.

[0158] As described above, this invention aims to achieve multi-source information fusion and automated in-depth summarization of similar enterprise cases. The system first retrieves basic case data, expert evaluations, and online materials based on case identifiers. Then, it extracts key facts, viewpoints, and supplementary details from each source. By aligning expert and online perspectives with the basic case framework and resolving potential conflicts according to preset priorities, the system can extract deep core insights into the success or failure of the case. Finally, based on a preset structured template, the system integrates and populates the core facts of the case with the extracted insights to generate a standardized case summary containing "core framework," "multi-dimensional analysis," and "transferable implications." This provides a comprehensive and clearly defined decision-making reference for subsequent simulation coaching and comparison.

[0159] S16: Based on the structured case summary and in-depth questions, initiate the guided question-and-answer process. The guided question-and-answer process includes:

[0160] S61: Based on the decision-making scenarios and business domains involved in the in-depth question, instantiate multiple predefined virtual roles, and configure each role with a stance, knowledge background, and dialogue objectives that match the scenario and domain. The specific process is as follows:

[0161] S611: Select role templates from the virtual role library that are relevant to the business area and decision-making level of the problem. The role templates include at least internal role templates and external role templates. Internal role templates include, but are not limited to, CEO, CFO, marketing director, product director, and technical lead. External role templates include, but are not limited to, potential customers, investors, partners, and industry experts.

[0162] S612: Load basic attributes for each selected role template. The basic attributes include role name, core responsibilities, knowledge domain, decision preference weight, and initial emotional state.

[0163] S613: Based on the specific context of the in-depth problem and the conflict points reflected in the structured case summary, dynamically adjust and finally determine the specific position of each virtual character and the phased goals of this dialogue. Among them, the goal of at least one virtual character is configured to partially conflict with or challenge the user's preset goal.

[0164] S62: Based on in-depth questions and structured case summaries, a simulated scenario is constructed. Multiple virtual characters interact with the user in multiple rounds, guiding the user to make specific decisions and responses regarding pre-set phased goals within the simulated scenario. The specific process is as follows:

[0165] S621: Scenario initialization, starting with the in-depth problem, combines key information from the core context of the structured case summary to generate an initial simulation scenario description including time, location, current predicament, and initial resource constraints;

[0166] S622: Question and guidance generation. Based on the current state of the simulation scenario and the position and goals of the activated virtual characters, generate specific questions, decision options and scenario descriptions to be presented to the user. The questions and scenarios are designed to guide the user toward specific business goals and expose their decision-making blind spots.

[0167] S623: Decision collection, receiving and parsing the text and choice-based answers given by users to questions and situations raised by virtual characters, and formalizing them into standardized decision instructions;

[0168] S624: Role reaction generation. Based on the user's decision instructions, multiple other virtual roles affected by the decision are activated. Based on their own positions and goals, these roles generate expressions of agreement, questioning, supplementary questions, and new situational challenges in response to the user's decision, forming a multi-round dialogue loop.

[0169] S63: Based on a pre-set business rule model, the system performs real-time impact analysis on user decisions or responses in each round of interaction, updates the state of the simulated scenario, and generates the final simulation result based on the accumulated decision sequence from multiple rounds. The specific process is as follows:

[0170] S631: The Business Rules Model is a quantitative simulation engine that includes multiple sub-modules such as finance, marketing, operations, and human resources. Each sub-module contains rules, formulas, and parameters that reflect business causal relationships.

[0171] S632: The standardized decision instructions made by the user and the reactions of other virtual characters are taken as inputs and fed into the business rule model;

[0172] S633: The business rules model calculates and outputs the impact values ​​of the decisions and events in this round on a set of key business indicators based on the input. The key business indicators include at least cash flow, market share, customer satisfaction, team morale and product development progress.

[0173] S634: Based on the calculated impact value, update the overall state description of the simulation scenario, and determine whether the preset number of interaction rounds has been reached or a specific ending event has been triggered, thereby ending the simulation and generating simulation results containing the final values ​​of various business indicators.

[0174] S64: Compare the simulation results with the real-world enterprise results recorded in the structured case summary, and guide users to make optimization decisions based on the comparison. The specific process is as follows:

[0175] S641: Results Comparison. The key business indicators and decision-making paths in the simulation results are presented side by side with the actions taken by real enterprises and their corresponding final results in the structured case summary, highlighting the differences.

[0176] S642: Attribution analysis prompts automatically generate attribution analyses for key nodes in simulation results based on interpretable data within the business rule model, prompting users which decisions played a decisive role and linking them to the multi-dimensional analysis section in the case summary;

[0177] S643: Optimization guidance, based on differences and attribution analysis, presents users with specific reflection questions or suggested decision points for adjustment, and provides the function of returning to a specific round in S62 to make choices again and observe different results, thereby guiding users to complete decision optimization.

[0178] As described above, this invention aims to deepen users' understanding and decision-making abilities regarding complex business problems by creating an immersive decision-making simulation environment that places users in multi-role, multi-round interactive deduction. The system first instantiates multiple virtual roles (such as CFO and client) with different stances and goals from a role library based on the scenario of the deepened problem, and configures specific stances and challenging goals that fit the context. Then, based on the deepened problem and case summary, an initial simulation scenario is constructed. The virtual roles sequentially present users with specific problems, scenarios, and challenges, guiding them to make a series of decisions. Each user decision, along with the virtual role's reaction, is input in real-time into a quantitative model embedded with business rules and causal relationships. This model calculates the impact of the decision on key indicators such as cash flow and market share, and dynamically updates the scenario state, forming multiple rounds of interactive loops, ultimately generating simulation results. These results are then visually compared and analyzed with real-world case results. The system then guides users back to key decision points to re-select, helping users explore the consequences of different decision paths through an iterative process of "decision-deduction-feedback," thereby optimizing decision-making logic.

[0179] S17: Compare and analyze the simulation results with the real-world results in the structured case summary, and guide users to optimize their decisions and responses based on this, generating final coaching conclusions and optimization suggestions, specifically including:

[0180] S71: Compare and analyze the simulation results under the user's decision-making path with the actual results corresponding to the real enterprise action paths recorded in the structured case summary from multiple dimensions, and generate a difference analysis report. The specific process is as follows:

[0181] S711: Data indicator comparison, extract the quantitative business indicators from the simulation results, and compare them with the corresponding indicators in the final results section of the structured case summary. Present them side by side in a visual representation and calculate the difference value. The business indicators should include at least the input cost, revenue growth rate, market share change and key project completion cycle.

[0182] S712: Decision Path Comparison. This feature compares a series of decisions made by the user during the simulation with the key action sequence taken by the real company in the case study, in the form of a timeline, highlighting the divergence points of the paths.

[0183] S713: Context and Hypothesis Comparison: Analyze and compare the similarities and differences between the initial conditions and external environment assumptions of the simulated scenario and the background of the real case, and evaluate their impact on the comparability of the results;

[0184] S714: Generate a difference analysis report that integrates the above comparison results. This report clearly points out the advantages, risks and main differences of the user path compared to the case path.

[0185] S72: Based on the difference analysis report and the core insights from the structured case summary, generate attribution explanations and optimization suggestions for the user's decision-making path. The specific process is as follows:

[0186] S721: Attribution Explanation. This involves calling the interpretability module of the business rule model used in the guided question-and-answer process to analyze the main decision factors that cause key differences between simulation results and real-world case results, and linking them to the identified decision divergence points to form an attribution explanation.

[0187] S722: Recommendation generation, based on the difference analysis report and attribution explanation, and integrating the multi-dimensional analysis and transferable implications from the structured case summary, to generate a set of actionable optimization recommendations;

[0188] S723: Optimization suggestions are categorized as follows:

[0189] Strengthen recommendations by focusing on aspects of user decision-making that are superior to case studies;

[0190] The adjustment recommendations target aspects of user decision-making that pose potential risks or have poor results.

[0191] Additional recommendations for important actions that users may not have considered but which are revealed by the case studies;

[0192] S73: Initiate an interactive decision optimization guidance process, allowing users to reselect at key decision points and observe new simulation results. The specific process is as follows:

[0193] S731: Present users with a difference analysis report, attribution explanations, and preliminary optimization suggestions;

[0194] S732: Guides users to review key decision points during the simulation process and provides the opportunity to return to any selected decision point and make a new choice;

[0195] S733: After a user makes a new choice at a certain decision point, based on the business rule model, a rapid re-deduction is performed from that point to generate new branch simulation results;

[0196] S734: Compare the new branch simulation results with the original simulation results and the actual results of the case again to provide immediate feedback on the impact of the change in selection;

[0197] S74: Based on the final simulation path and optimization selection, and drawing insights from the structured case summary, generate a structured final coaching conclusion and conclude this round of coaching sessions. The specific process is as follows:

[0198] S741: Integrate the following information: the user's initial in-depth question, the optimized final decision path, the final simulation results under this path, the core insights compared with the case, and the optimization suggestions confirmed by the user and recommended by the system;

[0199] S742: Generate the final coaching conclusion according to the preset template. The template shall include at least the following sections: problem restatement and diagnostic summary, deduction of decision path and analysis of key nodes, core takeaways from the comparison case, specific action suggestions for users, and suggestions for follow-up and learning.

[0200] S743: Output the final coaching conclusions in a structured document format, and provide functions for saving, exporting, and linking with subsequent coaching sessions.

[0201] As described above, this step aims to transform simulation results into personalized decision-making insights and action plans through systematic comparison, attribution, and iterative optimization. The system first compares the user's simulation results with the actual results of real-world cases from multiple dimensions, generating a difference analysis report that clearly presents the similarities and differences in quantitative indicators, decision-making paths, and background assumptions. Subsequently, combining the interpretability analysis of the business model with the core insights of the case study, it generates attribution explanations and classification optimization suggestions (strengthening, adjusting, and supplementing). Next, the system guides the user into an interactive optimization loop, allowing them to revisit any key decision point and re-select, instantly observing the simulation results under the new decision path, deepening learning through immediate feedback. Finally, the system integrates the user's initial questions, the optimized decision path, comparative insights, and adopted suggestions to generate a structured final coaching conclusion document, covering diagnosis, analysis, gains, specific actions, and follow-up plans, thus completing a closed loop from simulation practice to cognitive improvement and action planning.

[0202] like Figure 2As shown, this embodiment also provides an innovation and entrepreneurship guidance question-and-answer matching system, including:

[0203] The user interface module is used to receive user input data and questions;

[0204] The user profile building and analysis module is used to build and analyze dynamic user profiles based on user data;

[0205] The intent understanding and question refinement module is used to generate refined questions based on user questions and dynamic user profiles.

[0206] The case retrieval and matching module is used to retrieve similar cases from the enterprise case knowledge base based on in-depth questions;

[0207] The multi-source information fusion and summarization module is used to fuse multi-source information on similar cases and generate structured case summaries;

[0208] A guided simulation coaching engine is used to guide users in decision-making simulations and generate simulation results through virtual roles, based on in-depth questions and structured case summaries.

[0209] The comparative analysis and optimization generation module is used to compare and analyze the simulation results with the results in the structured case summary, and generate coaching conclusions and optimization suggestions.

[0210] Example 1

[0211] The founder of a smart home hardware startup raised the coaching question, "The production cost of the new product exceeded the budget, resulting in an uncompetitive selling price. Should we seek a cheaper contract manufacturer?" The system received this question along with the user's proactively uploaded business plan, detailed financial data, market research report, and historical interaction records. During the user profile construction and analysis phase, the system preprocessed and correlated multi-source data, identifying the user's core dilemma as a "supply chain cost and quality balance dilemma." Attribution analysis revealed that the root cause was the team's lack of supply chain management experience and over-reliance on negotiating with a single supplier. A capability assessment model quantified a significant weakness in the "operations management" dimension. In the intent understanding phase, the system, based on this profile, conducted an in-depth analysis of the surface problem, deducing that the user's true intent was to systematically optimize costs without compromising product quality and brand positioning. The system integrated constraints such as "lack of team experience" and "mid-to-high-end product positioning" to generate a structured, in-depth question, clarifying the shift from the single demand for a cheaper contract manufacturer to exploring multi-path cost optimization decisions, including negotiation and value engineering, while ensuring quality.

[0212] Based on this in-depth problem, the case retrieval module matched highly similar cases from the knowledge base: a drone startup successfully reduced costs and ensured quality through a combination of strategies including "renegotiating with existing suppliers + introducing a second supplier for replaceable parts + designing replacements for non-core components." The multi-source information fusion module retrieved the basic data of this case, integrated expert evaluations and online materials, and generated a structured summary. Its core insight is "adopting a tiered and categorized supply chain optimization strategy to avoid completely switching suppliers for price reductions."

[0213] Subsequently, the guided simulation coaching engine was activated. The system instantiated several virtual roles based on the scenario, including a CFO committed to cost reduction, an operations manager concerned about quality, and a contract manufacturer's sales manager. It also constructed a simulated decision-making meeting scenario based on in-depth questions and case summaries. The engine guided users to make specific choices through multiple rounds of interaction. For example, when faced with initial options such as "completely switching to a cheaper supplier," "negotiating with the current supplier," or "initiating design changes," different roles would offer agreement, questioning, or supplementary information based on their own perspectives. Each user decision was input into an embedded business rule model, which continuously simulated and updated key indicators such as "product cost," "quality stability," and "team confidence." After multiple rounds of simulation, the user developed a combined strategy, with results showing that cost reduction targets were met while quality risks remained manageable.

[0214] In the comparative analysis and optimization phase, the system compares the simulation results with retrieved real-world case results from multiple dimensions, generating a difference analysis report. The report indicates that the user's simulated path is more robust than the case studies, with a slightly lower cost reduction but less quality fluctuation. The system then guides the user back to key decision points such as "whether to introduce a second supplier" for re-selection, and quickly deduces the different impacts of the new choices. Finally, the system integrates all information to generate a structured final coaching conclusion that includes decision path analysis, lessons learned from the case studies, and specific phased action suggestions, completing a full closed-loop coaching process from problem identification and simulation practice to reflection and optimization.

[0215] Example 2

[0216] A chain bakery entrepreneur raised the question of whether to increase the online marketing budget due to poor offline sales of their new healthy snack products. The system received the question and simultaneously retrieved uploaded store sales data, new product promotion plans, consumer trend reports, and historical inquiry records. After analyzing the data, the user profiling module identified the core challenge as "insufficient market acceptance of the new product." Attribution analysis revealed that the root cause was not insufficient marketing budget, but rather a mismatch between the product positioning and the existing stores' primary consumption scenarios, and a team overemphasis on offline operations. The capability map clearly showed shortcomings in "marketing" and "product definition" for new channels. The intent understanding module linked this surface-level request with deeper user profile information, deducing that the user's true intent was to increase the penetration rate of the new product among the target customer group, constrained by the "misalignment between offline customer traffic attributes and online target customer groups." The resulting in-depth question transformed a simple question about increasing the budget into a systemic decision-making problem exploring how to find a suitable "scenario-channel" combination for the new product.

[0217] Based on this, the case retrieval module matched a similar case from the knowledge base: a traditional tea brand initially encountered a lukewarm reception when promoting sugar-free bottled tea offline. Later, by repositioning it as a "healthy office drink," partnering with convenience stores in office buildings, and focusing on corresponding scenarios on social media, it successfully opened up the market. The multi-source information fusion module summarized this case, extracting the core insight that "when a new product does not match existing channels, one should prioritize finding or building a new combination of scenarios and channels."

[0218] The guided simulation coaching engine then created a simulated board meeting scenario featuring multiple roles, including a conservative offline operations manager, an aggressive online marketing consultant, a target customer representative, and an investor. During the interaction, users were guided to make decisions among options such as "increasing the online budget," "cutting product lines," and "adjusting channel strategies," and faced challenges from different perspectives (e.g., customer feedback like "I want to buy breakfast in the store, but healthy snacks feel like overtime food"). Based on the user's series of choices, the business rules model calculated and updated the status of simulated indicators such as "online sales," "brand consistency," and "cash flow" in real time. Ultimately, the user simulated a decision path of "not significantly increasing the advertising budget for now, but instead placing the product in high-end office building convenience stores and making minor adjustments."

[0219] In the final stage, the system compares the simulated path with the real path of the tea beverage brand case, visually presenting the differences in the aggressiveness of the channel strategies and the results. Based on attribution analysis, the system generates optimization suggestions and allows users to backtrack to the "Channel Partner Selection" node to try out the path of cooperating with large online platforms and observe its different impacts on sales and profits. Through this comparison and backtracking, the system ultimately generates a conclusion document that affirms the rationality of the user's conservative strategy and provides specific negotiation suggestions based on data deduction for its next possible expansion, thus achieving in-depth, personalized, and actionable entrepreneurial guidance.

[0220] The embodiments of the present invention have been described in detail above with reference to the accompanying drawings. However, the present invention is not limited to the above embodiments. Within the scope of knowledge possessed by those skilled in the art, various changes can be made without departing from the spirit of the present invention.

Claims

1. A question-and-answer matching method for innovation and entrepreneurship guidance, characterized in that: Includes the following steps: S11: Receive the current tutoring question and related user data input by the user; S12: Based on user data, construct and analyze dynamic user profiles. The user profiles include the user's capability graph, current predicament, and predicament attribution analysis. The capability graph refers to a structured data table formed by quantitatively evaluating the user's capability scores in multiple preset business areas, dynamically reflecting the relative strength of the user's capabilities in each area. S13: Combining the current tutoring questions with the dynamic user profile, classify the questions and deduce the user's true intentions to generate a more in-depth question with context and constraints; S14: Based on the type and characteristics of the in-depth questions, retrieve and extract similar enterprise cases from the pre-built enterprise case knowledge base; S15: Extract similar enterprise cases, integrate expert evaluations and related network materials, and automatically summarize and categorize them to generate structured case summaries; S16: Based on the structured case summary and in-depth questions, initiate the guided question-and-answer process; S17: Compare and analyze the simulation results with the real-world results in the structured case summary, and guide users to optimize their decisions and responses based on this, generating the final coaching conclusions and optimization suggestions.

2. The innovation and entrepreneurship guidance question-and-answer matching method according to claim 1, characterized in that: When building and analyzing dynamic user profiles based on user data, the specific steps include: S21: Receive and preprocess various types of input data from the user; S22: Based on the preprocessed data, identify and define the core dilemmas currently faced by the user; S23: Conduct attribution analysis on the core dilemmas to identify the root causes of each dilemma and related secondary issues; S24: Based on input data and attribution analysis results, construct and update the user's capability map in each preset business area; specifically: define multiple preset business capability dimensions such as strategic planning, product development, marketing, sales management, operations and production, financial management and team building; design a set of evaluation indicators for each capability dimension, and extract evidence data related to each evaluation indicator from the user information database and attribution analysis report; use the capability assessment model to quantify the evidence data and calculate the user's current score in each capability dimension; integrate the scores of all capability dimensions and present them in the form of a structured data table to form the user's capability map; S25: Integrate core dilemmas, root causes, secondary problems, and capability maps to form a structured dynamic user profile, and output it for subsequent intent understanding and case matching.

3. The innovation and entrepreneurship guidance question-and-answer matching method according to claim 2, characterized in that: When combining current tutoring questions with dynamic user profiles to categorize questions and deduce users' true intentions, generating a more in-depth question with context and constraints, the specific steps include: S31: Receive the current tutoring question text input by the user and obtain the constructed dynamic user profile; S32: Automatically categorize the current tutoring question text to determine its question category and related business area; S33: Combining dynamic user profiles, conduct in-depth analysis of the current coaching questions that have been categorized to deduce the user's true intentions; S34: Integrate the question category, relevant business areas, the user's true intent, and specific constraints extracted from the dynamic user profile to generate a structured, in-depth question containing a complete decision-making context; S35: Output in-depth questions for use in subsequent case matching and simulated coaching processes.

4. The innovation and entrepreneurship guidance question-and-answer matching method according to claim 3, characterized in that: Deducing a user's true intent specifically includes: S331: Analyze dynamic user profiles to extract users' skill gaps, current core difficulties, and their root causes; S332: Map and analyze the contradictions between the surface demands of the current coaching problem and the extracted skill gaps, current core dilemmas, and root causes; S333: Based on association mapping and contradiction analysis, identify users' unstated needs, potential concerns and decision priorities. Unstated needs include the need for solutions that users have not been aware of due to their own limitations. Potential concerns include worries about the root causes of the predicament. S334: Generate a textual description of the user's true intent by integrating surface-level appeals, unstated needs, and potential concerns.

5. The innovation and entrepreneurship guidance question-and-answer matching method according to claim 4, characterized in that: When retrieving and extracting similar enterprise cases from a pre-built enterprise case knowledge base based on the type and characteristics of the in-depth questions, the specific steps include: S41: Receive the in-depth question generated by the intent deduction module. The in-depth question includes the question category, main business domain, related dilemmas, true intent and specific constraints. S42: Analyze and deepen the problem, and extract multi-dimensional retrieval feature vectors for case retrieval; S43: Based on multi-dimensional retrieval feature vectors, perform multi-level retrieval and similarity calculation in a pre-built enterprise case knowledge base; S44: Based on the similarity calculation results, optimize the sorting and filtering of the retrieved candidate cases; S45: Extract at least one most similar business case from the sorted results and output its complete or summary information to the subsequent summary module.

6. The innovation and entrepreneurship guidance question-and-answer matching method according to claim 5, characterized in that: Each case in the pre-built enterprise case knowledge base is stored in a structured format and includes at least the following index fields: case ID, enterprise background description, industry, development stage, core problem description, action description, final result, and pre-computed case semantic vector; among which, multi-level retrieval and similarity calculation specifically include: S431: Preliminary screening uses structured search keys in multi-dimensional search feature vectors to perform precise or fuzzy matching in the knowledge base, quickly filtering out a preliminary set of candidate cases that are basically matched in dimensions including problem category, business domain, industry, and development stage. S432: Refined calculation: For each case in the preliminary candidate case set, calculate the cosine similarity between its case semantic vector and the semantic feature vector in the multi-dimensional retrieval feature vector to obtain a semantic similarity score. S433: Comprehensive score, which combines semantic similarity score and weighted score based on structured tag matching degree, and generates a comprehensive similarity score for each candidate case through weighted calculation.

7. The innovation and entrepreneurship guidance question-and-answer matching method according to claim 6, characterized in that: When extracting similar enterprise cases, integrating expert evaluations and related online materials, and automatically summarizing and generating structured case summaries, the specific steps include: S51: Receive identification information of similar enterprise cases; S52: Based on the identification information, obtain the basic case data of the corresponding enterprise case, and at the same time obtain the expert evaluation data and related online materials associated with the case; S53: Extract key information elements related to the case from basic case data, expert evaluation data, and related online materials respectively; S54: Integrate, align, and resolve conflicts among key information elements extracted from different sources, and then conduct inductive analysis to extract core insights. S55: Based on the preset structured summary template, integrate and fill in the key information elements and core insights of the basic case data to generate a structured case summary; S56: Output a structured case summary for subsequent guided question answering and result comparison.

8. The innovation and entrepreneurship guidance question-and-answer matching method according to claim 7, characterized in that: The guided question-and-answer process includes: S61: Based on the decision-making scenarios and business domains involved in the in-depth problem, instantiate multiple predefined virtual roles, and configure each role with a stance, knowledge background and dialogue objectives that match the scenario and domain; S62: Construct a simulated scenario based on in-depth questions and structured case summaries, and guide the user to make specific decisions and answers for preset phased goals in the simulated scenario through multiple rounds of interaction with multiple virtual characters. S63: Based on a preset business rule model, it performs real-time impact analysis on the decisions or answers made by users in each round of interaction, updates the state of the simulated scenario, and generates the final simulation result based on the cumulative decision sequence of multiple rounds. S64: Compare the simulation results with the real enterprise results recorded in the structured case summary and output them to guide users to make decision optimizations based on the comparison.

9. The innovation and entrepreneurship guidance question-and-answer matching method according to claim 8, characterized in that: When comparing and analyzing simulation results with real-world results in structured case summaries, and guiding users to optimize their decisions and responses based on this, to generate final coaching conclusions and optimization suggestions, the specific steps include: S71: Compare and analyze the simulation results under the user's decision-making path with the actual results corresponding to the real enterprise action paths recorded in the structured case summary from multiple dimensions, and generate a difference analysis report; S72: Based on the difference analysis report and the core insights in the structured case summary, generate attribution explanations and optimization suggestions for the user's decision-making path; S73: Initiate an interactive decision optimization guidance process, allowing users to reselect at key decision points and observe new simulation results; S74: Based on the final simulation path and optimization choice, and drawing inspiration from the integrated structured case summary, generate a structured final coaching conclusion and conclude this round of coaching sessions.

10. An innovation and entrepreneurship guidance question-and-answer matching system, used to implement the innovation and entrepreneurship guidance question-and-answer matching method according to any one of claims 1-9, characterized in that: include: The user interface module is used to receive user input data and questions; The user profile building and analysis module is used to build and analyze dynamic user profiles based on user data; The intent understanding and question refinement module is used to generate refined questions based on user questions and dynamic user profiles. The case retrieval and matching module is used to retrieve similar cases from the enterprise case knowledge base based on in-depth questions; The multi-source information fusion and summarization module is used to fuse multi-source information on similar cases and generate structured case summaries; A guided simulation coaching engine is used to guide users in decision-making simulations and generate simulation results through virtual roles, based on in-depth questions and structured case summaries. The comparative analysis and optimization generation module is used to compare and analyze the simulation results with the results in the structured case summary, and generate coaching conclusions and optimization suggestions.

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