Artificial intelligence-based talent matching method and system

Abstract

The invention discloses a talent matching method and system based on artificial intelligence, and aims to improve human resource configuration efficiency and decision intelligence. The method comprises the following steps: collecting talent and demand data in multiple channels, especially unstructured communication data including interview records and work communication records; processing data through technologies such as multi-mode resume analysis, extracting features and fusing the features; an enterprise talent knowledge base which is used for continuous learning and dynamic maintenance based on a system operation result and multi-source feedback and integrates structured and unstructured data is constructed; based on the natural language query of the user, providing intelligent question answering and decision support by using a retrieval enhancement generation model connected with the specific enterprise talent knowledge base; an advanced deep learning algorithm is adopted, a knowledge base is combined to carry out man-post matching and generate recommendation of context perception, and self-optimization of a matching strategy is realized through mechanisms such as reinforcement learning and the like. According to the invention, accurate and dynamic talent matching and intelligent decision making can be realized.

Description

Technical Field

[0001] The present invention relates to the field of artificial intelligence application technology, and more specifically, to an artificial intelligence-based talent matching method and system. Background Art

[0002] In today's rapidly evolving business environment, companies are placing increasing demands on the accurate identification and efficient allocation of high-quality talent. Traditional talent matching and management methods, as well as existing talent matching systems, face numerous challenges in practical application.

[0003] First, many existing talent matching systems rely primarily on matching keywords found in resumes and job descriptions. While simple to implement, these methods often lack a deep understanding of the text's semantics, resulting in low matching accuracy. This can lead to missed candidates who possess the required skills but don't use specific keywords, or incorrectly recommending irrelevant candidates.

[0004] Secondly, some systems that incorporate machine learning or basic natural language processing technologies have improved matching results to a certain extent. However, these systems may still rely on large amounts of high-quality annotated data, have limited model generalization capabilities, or fail to fully consider deeper factors such as the dynamic development potential of talent and long-term compatibility with corporate culture.

[0005] More importantly, existing technologies generally suffer from limitations in data application. For example, internal structured talent data (such as employee basic information and performance appraisal results) and large amounts of unstructured communication data (such as interview records, internal work communications, and collaborative feedback) are often separated, lacking effective collaborative analysis mechanisms. This makes it difficult for companies to develop a comprehensive understanding of talent and results in inefficient information utilization. Furthermore, most systems employ a static matching model that struggles to adapt to the dynamic changes in talent capabilities and career plans, as well as the real-time adjustments to job requirements as business evolves. This results in poor timeliness of matching results and fails to meet the needs of dynamic management. Furthermore, existing systems are also insufficient in providing personalized talent development recommendations and building self-learning and dynamically updated talent knowledge bases. This makes it difficult to effectively feedback and optimize matching algorithms and knowledge systems based on actual recruitment results and subsequent employee performance. In terms of human-computer interaction, traditional systems often lack sufficiently intelligent and natural interaction methods, making it inconvenient for HR and managers to retrieve information and obtain decision support, impacting work efficiency.

[0006] Therefore, how to provide a talent matching method and system that can deeply integrate multi-source heterogeneous data, achieve accurate understanding and dynamic matching of talent and job requirements through advanced artificial intelligence technology, support natural language interaction for efficient decision-making, and build an enterprise-level talent knowledge management system with continuous learning and self-optimization capabilities has become a technical problem that urgently needs to be solved in this field. Summary of the Invention

[0007] The main purpose of the present invention is to provide an artificial intelligence-based talent matching method and system to address the shortcomings of the existing technologies proposed in the background technology in terms of data fusion, matching accuracy, dynamic adaptability, intelligent interaction, and knowledge base construction and updating, thereby significantly improving the efficiency of human resource allocation, optimizing the quality of management decisions, and reducing related costs.

[0008] To achieve the above objectives, the present invention provides an artificial intelligence-based talent matching method, comprising the following steps:

[0009] Data collection steps: Obtain candidate resume information, talent demand information of various departments of the company, performance and ability data of current employees, and unstructured data including interview records and work communication records from multiple channels;

[0010] Data processing: This involves processing the acquired data, including converting unstructured resume information into structured talent data using multimodal document understanding, named entity recognition, and semantic understanding technologies. This includes cleaning, deduplication, and standardization of various data types, extracting key features, and integrating data from different sources.

[0011] The intelligent analysis step involves building, continuously learning from, and dynamically maintaining an enterprise talent knowledge base based on feedback. This enterprise talent knowledge base integrates the previously processed structured talent data and unstructured communication data. Based on user queries entered through a natural language interactive interface, the search-enhanced generation model connected to the enterprise talent knowledge base is used to perform semantic understanding, knowledge retrieval, and enhanced content generation to provide intelligent question-answering and decision support. Furthermore, a deep learning matching algorithm is used, combined with information in the enterprise talent knowledge base, to analyze the match between talent and positions and generate recommendations.

[0012] The continuous feedback and optimization mechanism steps utilize the recruitment results, employee performance or user interaction feedback of the system operation to automatically adjust and optimize the content and association relationships of the enterprise talent knowledge base, as well as the matching strategy and model parameters of the deep learning matching algorithm.

[0013] The present invention, through the above technical solutions, especially through the construction of a unified enterprise talent knowledge base that integrates structured and unstructured data and has the ability of continuous learning and dynamic updating based on feedback, combines advanced natural language processing technology (such as multimodal resume parsing, retrieval enhancement generation based on large language models and enterprise-specific knowledge bases) and deep learning matching algorithms that can be self-optimized through reinforcement learning, to achieve a deep understanding and intelligent utilization of talent and job demand information. This method can not only significantly improve the accuracy and efficiency of person-job matching, effectively overcome the problems of data silos, static matching and insufficient semantic understanding in traditional methods, but also provide users with convenient and intelligent decision support through a natural language interactive interface. In addition, the system continuously improves the knowledge base and matching model through a clear and continuous feedback and optimization mechanism, thereby adapting to the dynamic changes of enterprises and talents, providing an efficient, intelligent and sustainable optimization solution for enterprise human resource management, and has a significant beneficial effect on improving the core competitiveness of enterprises and reducing operating costs. DETAILED DESCRIPTION

[0014] It should be noted that the relative arrangement of components and steps, the numerical expressions and numerical values ​​set forth in these embodiments do not limit the scope of the present invention unless specifically stated otherwise.

[0015] The following description of at least one exemplary embodiment is merely illustrative in nature and is in no way intended to limit the invention, its application, or uses.

[0016] Technologies, methods, and equipment known to ordinary technicians in the relevant art may not be discussed in detail, but where appropriate, the technologies, methods, and equipment should be considered part of the specification.

[0017] In all examples shown and discussed herein, any specific values ​​should be interpreted as merely exemplary and not limiting. Therefore, other examples of the exemplary embodiments may have different values.

[0018] The present invention provides an artificial intelligence-based talent matching method and corresponding system. By deeply integrating and intelligently analyzing multi-source heterogeneous data from within and outside the enterprise (particularly structured talent data and massive amounts of unstructured communication data that often contain rich implicit information, such as interview records, internal work discussions, and conversations in collaboration tools), this method constructs an enterprise talent knowledge base that is self-learning, dynamically evolving, and comprehensively reflects the capabilities and characteristics of talent. Based on this knowledge base, advanced natural language processing technologies (e.g., automatic parsing of resumes that support multiple formats and complex layouts, intelligent question-answering using retrieval-enhanced generation (RAG) based on large language models and incorporating enterprise-specific knowledge) and machine learning algorithms (e.g., deep learning matching models based on advanced architectures such as Transformers and continuously optimized using reinforcement learning) are utilized to achieve high-precision person-job matching, intelligent natural language question-answering and decision support, as well as personalized talent development and performance prediction services, thereby comprehensively improving the efficiency and intelligence of enterprise talent management.

[0019] The AI-based talent matching system of this invention adopts a layered architecture, logically divided into a data acquisition layer, a data processing layer, an intelligent analysis layer, an application service layer, and a user interaction layer. These layers work together to form a complete closed loop from data acquisition, processing, and analysis to intelligent application and interaction. A continuous feedback and optimization mechanism throughout the core intelligent analysis link drives the learning and evolution of the entire system.

[0020] 1. Data collection steps (performed by the data collection layer)

[0021] The data collection layer is responsible for collecting various raw data related to talent management from multiple data sources inside and outside the enterprise.

[0022] A resume data collection unit is responsible for acquiring candidate resume information from multiple channels. For example, it can configure an application programming interface (API) to automatically retrieve online resumes submitted by applicants from a company's official recruitment website; allow HR personnel to manually upload locally stored resume files in formats such as PDFs, Word documents, or images; and integrate data interfaces with mainstream third-party recruitment platforms to synchronize candidate resume data, ensuring a wide range of sources and timely updates. To ensure real-time data acquisition, a scheduled task mechanism can be implemented, combined with an incremental update strategy to reduce system load.

[0023] An enterprise demand collection unit is used to collect talent demand information from various business departments. It provides standardized job demand entry forms, allowing hiring managers or human resources business partners (HRBPs) to fill out detailed information online, including job responsibilities, qualifications, skill requirements, and salary ranges. This unit can also integrate with the job management or staffing modules within the company's existing human resource information system (HRIS) or enterprise resource planning (ERP) system to automatically synchronize job requirements and support tracking and versioning of changes to demand information. To extract key requirements from unstructured job descriptions, natural language processing technology can also be initially applied for analysis.

[0024] An employee performance data collection unit continuously collects performance and competency development data from current employees. It can be integrated with the company's internal performance management system to regularly synchronize information such as employee key performance indicator (KPI) completion status, annual or quarterly performance evaluation results, 360-degree environmental assessment feedback, competency assessment scores, completed training course records, and skill certifications obtained. During the data collection process, data desensitization is emphasized to effectively protect employee privacy.

[0025] An unstructured data collection unit focuses on collecting unstructured text and voice data that is crucial for understanding talent's soft skills, collaboration style, cultural adaptability, and tacit knowledge. For example, it can integrate the company's email system, internal instant messaging tools (such as WeChat for Business, DingTalk, etc.), and video conferencing system. Under the premise of complying with the company's data security and privacy policies, it can collect detailed text records and voice records of the interview process (which can be subsequently processed by voice-to-text technology), as well as text information generated by employees during project collaboration, work reports, team communication, and customer interactions. In addition, it can also support automatic text conversion of interview records in audio format, and perform preliminary data classification and labeling to prepare for subsequent data processing and analysis. The collection of this type of data is a key link in building a comprehensive talent portrait and a deep knowledge base.

[0026] 2. Data processing steps (performed by the data processing layer)

[0027] The data processing layer performs a series of refined processing and conversion on the raw data collected by the data acquisition layer, extracts valuable information, and forms a unified and standardized data view, laying a solid data foundation for subsequent intelligent analysis.

[0028] A core component in this layer is the resume parsing module, which aims to efficiently and accurately convert unstructured or semi-structured resume information from diverse sources and formats into structured talent data.

[0029] It may contain a multimodal document understanding unit. This unit can first support the parsing of multiple mainstream resume formats such as PDF, Word documents, TXT texts, HTML web pages and even pictures. For resumes in image format (such as scans or photos), it will call the integrated high-precision optical character recognition (OCR) engine to convert the text information in the image into an editable text stream. Subsequently, using document layout analysis technology (which may combine computer vision and rule engines, or adopt a layout analysis model based on deep learning), it can intelligently identify the overall area division of the resume, such as accurately distinguishing different logical blocks such as personal basic information module, education background module, work experience module, project experience module, skills list module, and self-evaluation module.

[0030] Next, a named entity recognition (NER) unit processes the text extracted from each block. This unit can be fine-tuned based on advanced deep learning models, such as the Bidirectional Long Short-Term Memory Network with Conditional Random Field (BiLSTM-CRF) model, or pre-trained language models based on the Transformer architecture (such as BERT and RoBERTa), to automatically identify and extract predefined key entity information from the resume text. These entity types typically include: name, gender, age, contact information (phone number, email address), university name, major, degree / education, enrollment and graduation dates; names of previous companies, positions held, and periods of employment; names of projects participated in, roles within those projects; and various professional skill keywords (such as "Java," "Python," "Financial Analysis," and "Project Management") and certifications obtained (such as "PMP" and "CPA"). To improve the recognition accuracy of domain-specific entities in resumes, this NER model is typically trained and optimized using a large corpus of resumes.

[0031] Then, a semantic understanding and relationship extraction unit uses deeper natural language understanding technology, such as large-scale pre-trained language models such as BERT, to deeply encode and understand the contextual semantics of the resume text. This allows the system to not only identify isolated entities, but also further extract the intrinsic correlation relationships between these entities, for example, in which period of work experience a specific skill was acquired and applied, and what are the main responsibilities and personal contributions of a project. This unit can also be committed to identifying information that may be implicit in the resume, such as by analyzing descriptive statements about job responsibilities, project results, or skill mastery (such as "proficient", "proficient", "led completion", "significantly improved XX% efficiency") to infer the actual application level of skills, the richness of experience, or the magnitude of contribution.

[0032] Finally, a knowledge graph construction unit is responsible for constructing the structured entities, attributes and their mutual relationships extracted in the previous steps into a formalized personal knowledge graph. This means that each candidate or employee in the system corresponds to a networked knowledge structure centered on him or her, which is associated with information such as his or her educational background, work history, project experience, and skills mastery. This unit can also be connected and referenced with the company's preset industry knowledge graph, standard skill dictionary, position system, etc., to achieve, for example, standardized mapping of skill names (unifying the diverse skill expressions in the resume into standard skill labels), hierarchical analysis of work experience, etc., and through graph reasoning technology, to a certain extent supplement information that is not explicitly mentioned in the resume but can be reasonably inferred.

[0033] Further functional features of the resume parsing module include: optional integration of information sources from other authorized channels (for example, public social media links provided by candidates, or public contribution records of code repositories such as GitHub) to form a more comprehensive and three-dimensional talent portrait; support for incremental updates to talent data, and automatically update existing structured data and knowledge graphs when new information about a person is obtained; provide a visual display of the parsing results, and explain the source and confidence of each extracted information; by adopting domain adaptation technology, the parsing model can also be optimized according to the resume characteristics of different industries (such as finance, IT, manufacturing) and different job types (such as technology, sales, management), in order to achieve higher parsing accuracy.

[0034] The data processing layer also includes a data cleaning and standardization module. This module is responsible for implementing strict data quality assurance processes for all types of raw data collected. Data cleaning operations are designed to identify and process noise, errors, and inconsistencies in the data, such as removing irrelevant special characters in the text, correcting obvious spelling errors or entry errors, and handling outliers. For missing data problems, they can be handled according to preset strategies, such as intelligently filling missing values ​​in non-critical fields (such as predictive filling based on context or existing data distribution), or directly marking missing key fields as unknown and prompting for supplementation. Data deduplication is achieved by designing multi-field-based fuzzy matching and similarity calculation algorithms (such as edit distance, Jaccard similarity, etc. combined with machine learning models) to accurately identify and merge duplicate candidate or employee records. Standardization operations are dedicated to converting heterogeneous data into a unified and standardized format and expression, such as unifying the representation format of dates and times, standardizing geographic location information (such as unifying to standard administrative division codes), standardizing academic level titles (such as unifying "master's students" into "Master"), and unifying job titles and skill labels (by connecting to industry standard dictionaries or enterprise-defined, dynamically maintained skill libraries).

[0035] In addition, the data processing layer has a feature extraction module. This module extracts key features valuable for subsequent intelligent analysis tasks from cleaned and standardized structured data, as well as some pre-processed unstructured data. This usually involves a series of feature engineering techniques, such as calculating total years of work experience and average length of service; vectorizing text features (for example, using pre-trained models such as Sentence-BERT to extract text embedding vectors that can capture deep semantics, or using the TF-IDF algorithm to extract keyword features); performing one-hot encoding or target encoding on categorical features; extracting trend features and periodic features from time series data; and extracting features such as sentiment, communication style, and key topics from unstructured communication data.

[0036] Finally, the data processing layer also includes a data fusion module. The core task of this module is to effectively associate and integrate various types of information about the same entity (talent or position) from different data sources, thereby constructing a unified, complete, and multi-dimensional data view. For example, it uses advanced entity resolution technology (which may be based on rules, machine learning, or graph methods) to associate multiple resumes, interview records, and employee records in internal systems for the same candidate; implements confidence-based data merging strategies to resolve data conflicts (such as inconsistent educational experience time in different resumes); supports incremental data fusion to update talent and position profiles in real time; and records data sources and processing processes through data lineage tracking to ensure data traceability and quality.

[0037] 3. Intelligent Analysis Steps (Performed by the Intelligent Analysis Layer)

[0038] The intelligent analysis layer is the core intelligence hub of the entire system. It receives high-quality, integrated data from the data processing layer and uses it to perform in-depth knowledge construction, intelligent matching and recommendation, as well as interactive Q&A and decision support. All functions of the intelligent analysis layer are closely integrated with a continuous feedback and optimization mechanism to achieve self-learning and evolution of the system.

[0039] The talent pool management module is responsible for building, storing, managing, and continuously learning and dynamically maintaining a comprehensive enterprise talent knowledge base based on feedback.

[0040] A distributed storage unit provides underlying storage support for the knowledge base, using distributed database technology that can be elastically expanded and supports massive data storage and high-concurrency access (for example, using NoSQL databases such as HBase and Cassandra to store unstructured and semi-structured data, combined with relational databases such as PostgreSQL to store structured data, or using NewSQL databases). It supports mixed storage of structured talent data, structured entities and relationships extracted from resumes and job descriptions, and original or processed unstructured communication data (such as interview texts and work communication summaries).

[0041] A multi-dimensional talent portrait unit, based on knowledge graph technology, constructs a dynamically updated, detailed, multi-dimensional talent portrait for each talent (candidate or current employee). Portrait dimensions may include hard skills (such as programming languages ​​and operating tools), soft skills (such as communication skills, teamwork, and leadership, some of which can be obtained through analysis of unstructured communication data), work experience (project details, responsibilities, and achievements), educational background, personality traits (such as inferred through professional assessments or communication data analysis), professional interests, learning acumen, etc., and supports historical version tracking and change analysis of talent portraits. This unit implements talent knowledge graphs based on graph databases (such as Neo4j and ArangoDB), efficiently storing and querying complex relationships between talent entities (such as the association between skills and projects, and collaborative relationships between colleagues).

[0042] A semantic indexing unit focuses on improving the efficiency and intelligence of information retrieval in the knowledge base. It can leverage vector database technologies (such as FAISS, Milvus, and Pinecone) to convert key text features in talent profiles (such as skill descriptions and project experience summaries), semantic fragments extracted from unstructured communication data, and core descriptions of job requirements into high-dimensional semantic vector representations through word embedding (such as Word2Vec and GloVe) or sentence embedding models (such as BERT and Sentence-BERT), and store them in the vector database. This enables the system to support efficient and fast retrieval based on semantic similarity, enabling functions such as fuzzy queries, similar talent recommendations, and "talent-to-talent" search.

[0043] A data security and privacy protection unit is responsible for ensuring the storage security, access compliance and proper use of all data in the talent knowledge base, including implementing data encryption technology (static encryption and transmission encryption), establishing and enforcing strict role-based access control (RBAC) mechanisms, and supporting data desensitization or pseudonymization of highly sensitive personal information (such as ID card numbers and part of contact information) in compliance with data protection regulations such as GDPR and CCPA.

[0044] One of the core innovations of the talent pool management module lies in its adaptive and dynamic knowledge base update capabilities. Rather than simply storing data statically, the module automatically adjusts the information in the knowledge base (e.g., the weighting of skill tags for specific positions, the correlation between certain traits in a talent profile and job success, and the confidence level of relationships between entities) based on recruitment results (e.g., whether a candidate passed the interview, their performance during the probationary period after being hired), current employee performance data, and various implicit or explicit feedback from users (HR and business managers) during system usage (e.g., whether they accepted, ignored, or manually adjusted the ranking of recommended results). For example, the system can leverage machine learning models to analyze the profiles of successful and high-performing employees and reversely optimize the identification of similar candidates and the weighting of relevant skills. Furthermore, the module supports the construction of a multi-granular talent classification system (e.g., by function, level, potential), recording time-series trends in various talent characteristics (e.g., skill improvement, experience accumulation), and optionally includes temporal feature analysis of talent characteristics to support potential prediction. It also explores the potential for knowledge and skills transfer between different fields, providing insights for cross-disciplinary talent development. This dynamic maintenance ensures the timeliness and accuracy of the knowledge base.

[0045] The talent-job matching module is the core engine in the intelligent analysis layer that is directly responsible for job matching and recommendation, and is equipped with a self-optimization mechanism based on system operation results and user feedback.

[0046] A two-way and multi-objective matching algorithm unit. This unit not only evaluates the talent's suitability for the position's skills, experience, and other conditions (Talent-to-Post), but also considers the attractiveness of the position itself to the talent (Post-to-Talent), such as development space, salary and benefits, corporate culture, etc. (if relevant data is available). The matching process can be modeled as a multi-objective optimization problem, aiming to balance multiple (potentially conflicting) matching dimensions. For example, maximizing skill matching while considering team diversity or candidate salary expectations. The algorithm supports the screening and filtering of hard conditions (such as education and minimum years of work experience) and the quantitative scoring and weighting of soft conditions.

[0047] A deep learning matching model unit can construct a deep matching model based on, for example, a Transformer architecture (such as BERT, RoBERTa, or a dual-tower / interactive model specifically designed for matching tasks). This model can perform deep semantic encoding of talent profiles (derived from a talent knowledge base) and job requirement profiles (also structured and stored in the knowledge base), either separately or jointly. It uses an internal attention mechanism (self-attention and cross-attention) to capture and weight the most critical and relevant matching points between the two. Model training can utilize historically successful matching cases (such as the onboarding resumes and job positions of high-performing employees) as positive samples, combined with a negative sampling strategy.

[0048] A context-aware recommendation unit enables matching recommendations to dynamically consider contextual factors such as the company's current development stage (startup, growth, or maturity), strategic priorities (such as expanding into new markets and increasing costs and efficiency), the specific team composition of the position to be filled (such as member skill complementarity and personality diversity), organizational culture, and the position's contextual relationships within the organization (such as reporting lines and collaborative departments). These factors can be input into the matching model as additional features or used to adjust the ranking and filtering of matching results.

[0049] A feedback learning and optimization mechanism unit gives the matching module the ability to continuously self-optimize. The system will record in detail the result data of each link in the entire recruitment process (such as resume screening pass rate, interview pass rate, offer acceptance rate, and new employees' 3 / 6 / 12-month performance) as well as the structured or unstructured feedback from HR and interviewers on candidate evaluations. This feedback data is used to: 1) adopt the reinforcement learning framework, treat recommendations as a sequential decision-making process, and use user behavior (clicks, favorites, marking inappropriate) as immediate rewards and long-term performance as delayed rewards to continuously optimize its recommendation strategy and model parameters; 2) regularly retrain or fine-tune the deep learning matching model to adapt to new data distributions and trends; 3) support A / B testing functions to verify the actual effects of different matching strategies, model versions, or feature combinations, thereby iteratively improving.

[0050] The innovations of the talent-job matching module also include: the ability to dynamically adjust the weight of each matching dimension in the comprehensive score based on the company's development stage and the urgency of the specific job; when making matching decisions, it not only considers the match between the individual and the job, but also further considers the candidate's collaboration potential and cultural fit with the target team (if supported by relevant data and models); a multi-step matching process of "rough screening-selection-sorting" can be adopted to improve accuracy while ensuring efficiency; the module can even combine the company's business development plan and historical talent flow data to predictively analyze possible job requirements in the future, and search and initially reserve corresponding talent resources in advance (i.e., predictive feeding).

[0051] The retrieval enhancement generation module (RAG enhancement module) is one of the core feature components in the system of the present invention for realizing advanced intelligent interaction and in-depth decision support. Its core lies in connecting to the enterprise talent knowledge base and using large language models to provide controllable, context-related intelligent question and answer and content generation based on enterprise-specific knowledge.

[0052] A knowledge search engine unit is responsible for building an efficient and intelligent search index for the company's talent knowledge base (including structured data, knowledge graphs, and vectorized text snippets). It supports a hybrid search model that combines semantic search (based on vector similarity, able to understand the deeper meaning of the query) with keyword search, and is capable of linking and retrieving knowledge across different data types (such as employee records, interview feedback text, and skill definitions). Search results are ranked based on factors such as relevance, timeliness, and authority.

[0053] A context understanding and question parsing unit is responsible for deeply understanding the queries or instructions entered by users through the natural language interface. It accurately identifies the user's core query intent (such as information search, talent screening, comparative analysis, and report generation), parses key entities and constraints in the query, maintains contextual coherence across multiple rounds of dialogue, and understands references and omitted expressions. For complex questions, this unit can also decompose them into multiple sub-questions that can be handled by the knowledge base or LLM.

[0054] A generative AI application unit is usually based on a powerful large language model (LLM), such as a model developed / fine-tuned by an enterprise. It receives processed user queries and relevant knowledge fragments retrieved from the knowledge base (as "enhanced context"). These retrieved knowledge fragments and the original query are organized into precise prompts and input into the LLM. Guided by these enhanced contextual information, the LLM generates natural, fluent and fact-based answers. Importantly, by controlling the context input to the LLM, the model's "hallucinations" can be effectively reduced, making its answers more focused on the real data and knowledge within the enterprise. The unit also supports multi-round dialogue interactions, allowing users to ask follow-up questions, clarify or refine their requirements.

[0055] A decision support functional unit enables the RAG module to provide various forms of decision support, such as: automatically generating a summary of a candidate's multi-dimensional comprehensive assessment report (integrating resume information, interview feedback, potential risks, etc.); horizontally comparing the strengths and weaknesses of different candidates in terms of key capabilities, experience matching, salary expectations, etc.; generating preliminary talent development planning suggestions for employees (combining their profiles, corporate development path data and job vacancies); and even supporting "what-if" scenario simulations (such as "If we adjust the skill requirements for a certain position, which internal employees may meet the requirements?").

[0056] The outstanding innovation of the RAG enhancement module lies in: deep integration of enterprise-specific talent management knowledge, policies and real-time data, rather than relying solely on the general knowledge of LLM; collaborative processing of structured data query results (such as employee lists obtained from the database) and unstructured text content generation (such as summarizing interview records or generating evaluation opinions); possessing a controllable generation mechanism, through carefully designed prompt engineering and post-processing rules, to ensure that the generated content complies with enterprise specifications, protects sensitive information, and provides traceability of the source of answers (for example, indicating which information comes from specific documents or records in the knowledge base); and being able to continuously learn from user interactions, by analyzing user satisfaction with generated answers, subsequent behavior, etc., to continuously optimize retrieval strategies, prompt templates and answer generation quality, and ensure the accuracy and compliance of generated content under the guidance of enterprise-specific knowledge and policies.

[0057] Feedback and optimization module / mechanism:

[0058] This is a modular mechanism or distributed capability that encompasses the core functions of the intelligent analysis layer (talent pool management, job matching, and RAG). Its core goal is to enable the system's self-learning, self-adaptation, and continuous evolution. It collects, processes, and utilizes feedback data from all aspects of system operation to drive knowledge base updates, matching algorithm iterations, and RAG model performance improvements. This feedback and optimization mechanism leverages recruitment results, employee performance, and user interaction feedback to automatically adjust and optimize the content and relationships of the enterprise's talent knowledge base, as well as the matching strategies and model parameters of the deep learning matching algorithm.

[0059] Specific working methods include but are not limited to:

[0060] 1. Dynamic adjustment and optimization of the knowledge base:

[0061] a. Feedback based on recruitment results: For example, if an employee with a certain profile who is recommended by the system and successfully hired subsequently demonstrates excellent performance, the skill tags, experience characteristics, and even soft skills reflected in unstructured communication associated with that profile will be given a higher weight in the knowledge base for similar positions. Conversely, if a match fails or the employee performs poorly after joining the company, the weight of the corresponding characteristics may be lowered or the correlation weakened.

[0062] b. Performance-based feedback: Continuously track the performance data of current employees and analyze its correlation with the talent profile created at the time of joining and the competency model in the knowledge base. If an employee's capabilities in a particular area improve significantly and this is reflected in their performance, the system will automatically update their profile and may trigger optimization of related skill definitions and competency hierarchy models.

[0063] c. Based on user interaction feedback: When HR or business managers use the RAG Q&A system or view matching recommendations, their actions of clicking, accepting, ignoring, or revising are recorded. For example, if a user frequently revises their understanding of a skill or the reason for a recommendation, the system uses this as a signal to optimize the corresponding entry in the knowledge base or the retrieval and generation strategy of the RAG model.

[0064] d. Integration of Time Series Analysis and Potential Prediction: The knowledge base not only stores the current status but also records the time series changes in various talent characteristics (such as skill mastery and project experience). Through time series analysis, the system can predict the talent's future development trajectory and potential. These predictions themselves become part of the knowledge base and are used to optimize matching and development path planning.

[0065] e. Dynamic adjustment of skill scarcity and association strength: Based on historical recruitment data, industry benchmark data, and current market supply and demand, the system can dynamically evaluate the scarcity of each skill in the knowledge base and adjust the association strength or recommendation priority between skills and specific positions.

[0066] 2. Matching algorithm and recommendation strategy optimization:

[0067] a. Model parameter and strategy iteration: Using a reinforcement learning framework, we use user behavior and recruitment results as reward signals to continuously optimize the parameters and recommendation strategies of the deep learning matching model. This includes adjusting feature weights, optimizing the focus of the attention mechanism, and improving candidate sorting logic.

[0068] b. Enhanced contextual adaptability: The system learns which matching factors are more important in different contexts (such as different departments, different recruitment urgency, and different market environments), thereby dynamically adjusting the logic of context-aware recommendations.

[0069] c. Adjustment of Bidirectional Matching and Multi-Objective Optimization Strategies: When generating recommendations, the system uses a bidirectional matching algorithm to comprehensively evaluate the talent's suitability for the position and the position's attractiveness to the talent. It also uses a multi-objective optimization algorithm to balance matching accuracy, recommendation diversity, and other business metrics (such as position urgency and salary range compliance). A feedback mechanism helps the system learn how to more effectively balance these objectives in different scenarios.

[0070] dA / B testing and new algorithm verification: Supports deploying different matching models or recommendation strategy versions for A / B testing, and verifies and selects the optimal solution through actual performance data (such as conversion rate and user satisfaction).

[0071] 3. Performance improvement of RAG model:

[0072] a. Retrieval accuracy optimization: If users frequently query but fail to obtain satisfactory answers from the knowledge base, or if the answers given by RAG do not meet the user's expectations, the system will analyze these failure cases and attempt to optimize the indexing method and semantic vector representation of relevant content in the knowledge base, or adjust the parameters of the retrieval algorithm (such as similarity threshold and recall strategy).

[0073] b. Improvement of generated content quality: Collect user evaluations of RAG-generated answers (e.g., whether they are useful, accurate, or complete). These evaluations can be used to fine-tune the prompt templates or generation parameters of large language models to make their answers more consistent with enterprise context and user expectations.

[0074] The implementation of this feedback and optimization module / mechanism typically relies on a robust data collection and analysis pipeline, user behavior tracking and modeling, and an automated model retraining and deployment framework. Its core value lies in transforming talent management from a static, rule-based process to a dynamic, data-driven, intelligent learning system capable of continuous adaptation.

[0075] 4. Application service steps (performed by the application service layer)

[0076] The application service layer encapsulates the core capabilities of the intelligent analysis layer into application services that are valuable to users.

[0077] A conversational query engine service unit, based on the RAG enhancement module of the intelligent analysis layer, provides an interactive portal that allows users to ask questions and retrieve information through natural language. This implementation utilizes an intent recognition model to understand user query intent (e.g., "Find candidates with Java and microservices experience," "Analyze the turnover of technical personnel in the past six months"), implements a slot-filling mechanism to extract key parameters from the query (e.g., skills: "Java," experience: "microservices"), designs a dialogue management strategy to support clarifying questions and multiple rounds of interaction (e.g., when a query is ambiguous, the system will ask, "In which city do you want these candidates' work experience?"), and employs a hybrid search strategy combining keyword and semantic search to obtain the most relevant information from the enterprise talent knowledge base.

[0078] A talent development path planning service unit focuses on providing personalized career growth support for internal employees of the enterprise. It comprehensively utilizes the multi-dimensional portraits of employees stored in the talent pool management module, the career development path data accumulated in the enterprise talent knowledge base (such as the successful promotion paths of employees with similar backgrounds in history), industry best practices, and the company's current strategic needs and job vacancies to provide employees with personalized career development advice and possible path planning. Its implementation can build a dynamic career development map based on industry talent development data and internal enterprise data, implement personal ability assessment algorithms to identify the gap between employees' current abilities and target job requirements, and design a path generator based on, for example, reinforcement learning or planning algorithms to recommend the best or multiple optional development paths (including recommended courses, participating projects, required mentor support, etc.), support multi-objective optimization (such as considering development speed, success probability, personal interests at the same time), and implement milestone setting and progress tracking.

[0079] A performance trend forecasting service unit leverages AI technology to conduct forward-looking analysis and predictions of employees' future performance. This unit utilizes time series forecasting models (such as LSTM, Prophet, or Transformer-based models) and inputs historical employee performance data, competency development trajectories, work engagement metrics (e.g., derived from communication data analysis), project participation, and relevant external factors (such as market changes and team adjustments) to predict an employee's likely performance trends over the next period (e.g., the next quarter or the next year). This system combines individual historical performance data with overall team performance, taking into account seasonal factors and business cycles, to achieve multi-timescale forecasts (short-term and medium-term). It also provides confidence intervals for the forecast results and analysis of key factors influencing the forecast, providing managers with a reference for early intervention or resource allocation.

[0080] A recruitment decision support service unit focuses on providing comprehensive, intelligent data support and decision-making assistance to the recruitment team and hiring department managers during the recruitment process. It can design a multi-dimensional evaluation indicator system to comprehensively evaluate candidates (integrating resume analysis results, interview feedback, online assessment data, etc.), generate decision recommendations based on case-based reasoning (CBR) (for example, referring to the historical hiring decisions and subsequent performance of candidates with similar backgrounds), support what-if analysis to simulate the potential impact (such as candidate acceptance rate, recruitment cycle, cost changes) of different recruitment decisions (such as adjusting salary, relaxing certain skill requirements), provide team composition analysis (assess the role of candidates in complementing or balancing the existing team's skills, personality, and experience after joining), and realize the recording and tracing of the decision-making process to support decision review and continuous improvement.

[0081] 5. User Interaction Steps (Implemented through the User Interaction Layer)

[0082] The user interaction layer is the portal for direct communication and operation between the talent matching system and end users (such as HR specialists, recruitment managers, business department heads, senior managers and even employees). It is closely connected with the application service layer and aims to provide an efficient, convenient and intelligent user experience.

[0083] A web application interface unit provides a user interface that can be accessed through a standard web browser. The interface adopts, for example, a front-end and back-end separation architecture (such as using modern frameworks such as React, Vue, or Angular on the front end, and Java, Python, Node.js, etc. on the back end) to implement responsive design to adapt to different screen sizes (desktop, tablet). Role-based interface customization can be designed to provide differentiated functional views and operation permissions for different users (such as HR, interviewers, and department managers). It supports rich data visualization components (charts, dashboards) to intuitively display analysis results. The display performance of large amounts of data (such as large candidate lists and complex talent portraits) is optimized through technologies such as progressive loading, virtual scrolling, and lazy loading.

[0084] For scenarios that may require deeper operating system integration, offline functionality, or specific performance requirements, a desktop application interface unit can be provided. For example, this can be built using cross-platform application development frameworks such as Electron and Tauri, supporting mainstream operating systems (Windows, macOS, and Linux). This can implement offline data caching (such as user preferences and commonly used query templates), support the use of some functions in weak network environments, and integrate system-level message push functionality to promptly remind users of important matters (such as interview arrangements and newly recommended candidates).

[0085] A standardized API interface unit provides a bridge for data exchange and functional integration between this system and other existing information systems of the enterprise (such as HRIS, OA, ATS, internal collaboration platform). The API interface follows, for example, the RESTful API design specification or GraphQL. Implement secure authentication mechanisms such as OAuth2.0 or OIDC, as well as authentication mechanisms based on JWT (JSON Web Tokens) or API Keys to ensure the security of data access. Support API throttling, circuit breaking and monitoring to ensure system stability and service quality. Provide detailed API documentation (such as generated using Swagger / OpenAPI specifications) and the necessary SDK (Software Development Kit) to facilitate integration by third-party systems or internal developers. And implement API version management to support smooth upgrades and backward compatibility.

[0086] A powerful data visualization unit is responsible for clearly and vividly presenting various complex analysis results, key performance indicators (KPIs), talent data distribution, matching recommendation details, etc. to users through a variety of chart formats (such as bar charts, line charts, pie charts, scatter plots, radar charts, etc. implemented using libraries such as ECharts, D3.js, and Chart.js), interactive dashboards, and intuitive talent maps or skill map visualizations. It supports interactive data exploration (such as drilling, filtering, sorting, and linked highlighting), allows users to customize dashboard layout and display content, and supports data export (such as CSV and Excel formats) and customized report generation.

[0087] System workflow and enterprise application examples:

[0088] A typical workflow of the artificial intelligence-based talent matching system of the present invention can be summarized as: data collection and preprocessing, dynamic talent knowledge base construction and continuous maintenance, intelligent matching and recommendation, intelligent interaction and decision support, and closed-loop feedback and optimization.

[0089] In a specific enterprise application case, for example, Company A, a technology-oriented innovative high-tech enterprise:

[0090] First, at the initial stage of system deployment, through demand analysis and system customization, we identified Company A's specific talent management pain points (such as large annual recruitment needs, time-consuming resume screening, and low efficiency in internal talent allocation) and specific needs.

[0091] Subsequently, during the data preparation and initialization phase, the system imported Company A's accumulated historical recruitment data, including approximately 10,000 resumes and related recruitment records. It also integrated internal employee data and unstructured data samples, such as historical, accessible interview records and project communication summaries, to build an initial corporate talent knowledge base. Furthermore, the resume parsing model was specifically trained based on Company A's job structure and skill requirements, and the company's job structure and skill dictionary were organized and standardized.

[0092] When the system went live, it integrated with Company A's existing HR system, OA system, and internal collaboration platforms (such as WeChat for Business and DingTalk). This enabled single sign-on and user permissions synchronization, and configured a process for collecting and accessing unstructured communication data (under regulatory compliance). Relevant users (such as the HR team and business department managers) were trained on system usage in batches and by role.

[0093] After the system was put into practical use, it achieved significant results in improving recruitment efficiency at Company A. For example, manual resume screening previously took an average of three days, but through the system's intelligent parsing and preliminary matching, this time was significantly reduced to 0.5 days. Because the system matches candidates based on more comprehensive data (including insights into unstructured communication data) and smarter algorithms (with self-learning and context-aware capabilities), the quality of recommended candidates has improved (for example, by 30%), which has directly led to a 25% increase in subsequent interview pass rates. The overall recruitment process has also been shortened by approximately 40%. More importantly, the RAG enhancement module allows HR and business managers to quickly query the talent pool using natural language, obtain in-depth candidate analysis reports and cross-sectional comparisons, and assist in interview decision-making, further improving efficiency and quality. These efficiency gains have reduced the workload of Company A's HR team by approximately 50%, allowing them to focus more on more strategic tasks such as improving the candidate experience and building the employer brand.

[0094] In terms of optimizing internal talent allocation, the system builds comprehensive employee profiles (including soft skills and collaborative styles extracted from work communications) and capability maps, making the potential and development direction of internal talent more clearly visible. This promotes the rational flow of talent within the organization (for example, an increase in internal talent turnover by 35%), improves the matching of employees with newly assigned positions (for example, an increase of 28%), and increases the adequacy of talent reserves in key positions (for example, an increase of 40%), thereby indirectly improving overall employee satisfaction (for example, an increase of 15%). Talent development path planning services also provide employees with a clearer growth direction.

[0095] In terms of enhanced decision-making support, the system enables Company A's management to access real-time talent data analysis reports and key indicator dashboards, providing strong data support for the company's talent strategy decisions. For example, the system's talent risk warning features (such as predicting the risk of key talent loss, which benefits from a comprehensive analysis of employee performance and communication data) are reportedly 85% accurate, and its predictions of future employee performance also reach 80% accuracy. Employee satisfaction with the talent development plans generated by the system, tailored to their individual circumstances and the company's development needs, has also significantly increased (e.g., by 45%).

[0096] Through continuous feedback and optimization, the system automatically adjusts the weighting of skills and experience descriptions related to "quick learning" and "complex problem-solving" based on the characteristics of technical employees successfully hired and who have performed well in the past year, making subsequent recommendations for similar positions more accurate. Furthermore, the RAG module optimizes the organization and retrieval of relevant content in the knowledge base based on HR's frequent query about "how to assess a candidate's innovation ability," generating more targeted assessment recommendations.

[0097] Applying the system of the present invention in a specific recruitment process:

[0098] During the recruitment needs analysis phase, the system not only collects standardized requirements from business departments through forms, but also uses natural language processing technology to automatically extract key elements from the unstructured demand description texts submitted by business departments, and automatically maps them to the company's preset standardized job templates and skill systems. The system can also provide optimization suggestions for currently submitted recruitment needs based on historical recruitment data and industry benchmark data, as well as the skill distribution of talents in the current knowledge base (for example, it can indicate that certain skill combinations are extremely scarce in the market and recommend adjusting or splitting the requirements). At the same time, the system can automatically analyze the supply and demand situation of the target position in the current talent market and generate a skill scarcity analysis report and salary reference.

[0099] During the resume screening and matching phase, the system first uses the resume parsing module to deeply analyze resumes from multiple channels, extracting key information and converting it into standardized structured data. Subsequently, the talent-position matching module calculates the candidate's compatibility with the target position across multiple dimensions and generates a comprehensive match score. This module utilizes contextual awareness. For example, if the team for a position urgently needs a specific supplementary skill, the match score for candidates possessing that skill will be dynamically increased. The system ultimately automatically recommends several candidates who best match the position requirements to the hiring manager, providing detailed and explainable justification for the recommendation.

[0100] During the interview and evaluation phase, the system can assist in intelligent matching of interviewers and automated coordination of interview arrangements. It can generate personalized interview outlines (based on the differences between job requirements and candidate resumes) and candidate background summaries for interviewers. After the interview, the system conveniently collects the interviewer's structured feedback on the candidate and associates the voice or text recordings of the interview process (if authorized) with these feedbacks for subsequent analysis and knowledge base accumulation. The RAG module can also be used to assist interviewers in asking questions in real time or quickly reviewing and summarizing the key answers of candidates after the interview. The system summarizes the results of multiple rounds of interviews to generate a comprehensive evaluation report.

[0101] During the decision support and recommendation phase, the system generates a preliminary recommendation on whether to hire based on the candidate's resume match, comprehensive evaluation results from multiple interview rounds, insights from the knowledge base regarding successful and unsuccessful recruitment for the position, and the job requirements. It also provides the average standards of historical hires for similar positions as a reference. The RAG module allows managers to conduct more in-depth "what-if" analysis, for example, "If we increase salaries by 5%, which previously unconsidered candidates might we attract?"

[0102] During the onboarding and training phase, the system can assist in generating personalized onboarding guidance plans and preliminary training programs based on the new employee's personal background, skills status, and specific requirements of the position they are joining (some suggestions may come from an analysis of the specific needs or concerns they demonstrate during the interview communication), and predict their future performance trends. This information will also be fed back into the knowledge base for future onboarding support for new employees with similar profiles.

[0103] Specific applications of the Retrieval Enhancement Generation (RAG) module in talent decision support:

[0104] First, we build a comprehensive, dynamically updated, enterprise-level talent decision-making knowledge base by integrating internal talent management documents, policies, best practices, historical recruitment case studies, employee performance reports, anonymized and aggregated internal communication data insights, expert experience libraries, and industry standards and regulatory requirements. This knowledge base is structured and efficiently indexed (including semantic vector indexing).

[0105] When users raise complex talent decision-making inquiries through natural language (for example, "How can we optimize our data scientist recruitment strategy to attract talent with experience in field X?" or "Based on Zhang San's recent project performance and communication history, is he suitable for promotion to team leader? Please provide reasons and potential risks." or "Compare the differences between candidates A and B in 'problem-solving ability,' and evaluate them based on their interview performance."), the RAG module understands and decomposes the query, employing multi-strategy knowledge retrieval (combining keywords, semantic similarity, and knowledge graph path queries) to accurately locate the most relevant structured data, text snippets, and historical cases from the company's talent knowledge base. Then, based on the retrieval results and the user's query context, it leverages a large-scale language model to generate natural language responses that include data support, strategic recommendations, personalized solutions, and even risk warnings. The system supports interactive clarification and multi-round dialogue to gradually refine solutions. For example, users can follow up with questions like, "What mitigation measures do we have for the risk points you mentioned?" Application scenarios cover recruitment strategy formulation, talent inventory and development planning, team building and optimization decision-making, performance issue diagnosis and improvement, and organizational culture assessment. Responses to the RAG module will provide references to information sources whenever possible to enhance transparency and credibility.

[0106] In summary, through the description of the above specific implementation methods, those skilled in the art can clearly understand how the present invention achieves significant improvements to traditional talent matching and management models through its unique system architecture, core AI algorithm module, and data-driven and feedback optimization mechanism throughout.

[0107] Although some specific embodiments of the present invention have been described in detail by way of examples, it should be understood by those skilled in the art that the above examples are for illustration only and are not intended to limit the scope of the present invention. It should be understood by those skilled in the art that modifications may be made to the above embodiments without departing from the scope and spirit of the present invention. The scope of the present invention is defined by the appended claims.

Claims

1. A talent matching method based on artificial intelligence, characterized in that: The following steps are involved: Data collection steps: Obtain candidate resume information, talent demand information of various departments of the company, performance and ability data of current employees, and unstructured data including interview records and work communication records from multiple channels; Data processing: This involves processing the acquired data, including converting unstructured resume information into structured talent data using multimodal document understanding, named entity recognition, and semantic understanding technologies. This includes cleaning, deduplication, and standardization of various data types, extracting key features, and integrating data from different sources. Intelligent analysis step: building and continuously learning and dynamically maintaining an enterprise talent knowledge base based on feedback. This enterprise talent knowledge base integrates the structured talent data and unstructured communication data processed above; Based on user queries entered through a natural language interface, the system uses a search-enhanced generation model connected to the company's talent knowledge base to perform semantic understanding, knowledge retrieval, and enhanced content generation to provide intelligent question-answering and decision support. It also uses a deep learning matching algorithm, combined with information from the company's talent knowledge base, to analyze the match between talent and positions and generate recommendations. The continuous feedback and optimization mechanism steps utilize the recruitment results, employee performance or user interaction feedback of the system operation to automatically adjust and optimize the content and association relationships of the enterprise talent knowledge base, as well as the matching strategy and model parameters of the deep learning matching algorithm.

2. The talent matching method based on artificial intelligence according to claim 1, characterized in that: In the data processing step, the step of converting unstructured resume information into structured talent data includes: Use optical character recognition technology to process resumes in image format to extract text information; Identify the structured areas of a resume using document layout analysis techniques; Use a deep learning-based named entity recognition model to identify and extract predefined key entity information from resume text; Use pre-trained language models to understand the semantics of resume text, extract relationships between entities, and identify implicit information; Build the extracted entities and relationships into a personal knowledge graph and connect it with the industry knowledge graph or skill dictionary to achieve standardized mapping; and, integrating information sources from external authorized channels to form a more comprehensive talent profile.

3. The talent matching method based on artificial intelligence according to claim 1, characterized in that: The steps to build and continuously learn and dynamically maintain the enterprise talent knowledge base based on feedback include: Use a distributed storage architecture to store integrated structured talent data and unstructured communication data; Build a multi-dimensional talent profile for talents based on knowledge graph technology, including hard skills, soft skills, experience, educational background, and personality traits, and support dynamic updates and historical version tracking of the talent profile; Vectorize talent characteristics and store them in a vector database to support efficient retrieval based on semantic similarity; And according to the recruitment results, employee performance or user feedback of the system operation, the information weight or correlation relationship in the enterprise talent knowledge base is automatically adjusted to achieve self-learning and dynamic updating of the knowledge base. The dynamic update includes time series characteristic analysis of talent characteristics to support potential prediction.

4. The talent matching method based on artificial intelligence according to claim 1, characterized in that: In the intelligent analysis step, the step of providing intelligent question answering and decision support by using a search enhancement generation model connected to the enterprise talent knowledge base includes: Perform intent recognition and semantic analysis on natural language queries input by users; Retrieving knowledge fragments related to the user query from the enterprise talent knowledge base; The user's original query and the retrieved relevant knowledge fragments are input into the large language model as enhanced context; The large language model generates natural language answers or decision suggestions based on the enhanced context, and ensures the accuracy and compliance of the generated content under the guidance of enterprise-specific knowledge and policies.

5. The talent matching method based on artificial intelligence according to claim 1, characterized in that: In the intelligent analysis step, the steps of using a deep learning matching algorithm to analyze the matching between talents and positions and generate recommendations include: Obtaining multi-dimensional talent profiles of the talent to be matched and demand profiles of the position to be matched from the enterprise talent knowledge base respectively; A deep matching model based on the Transformer architecture is used to encode the features of the talent profile and job requirement profile, and the matching score between the two is calculated through the attention mechanism; When generating recommendations, we consider the company's current development stage, team composition, and the position's contextual relationship within the organization to make context-aware recommendations. The user's interactive feedback on the recommendation results is recorded, and the feedback and optimization mechanism step uses a reinforcement learning mechanism to continuously optimize the matching strategy of the deep learning matching algorithm.

6. The talent matching method based on artificial intelligence according to claim 5, characterized in that: The step of generating recommendations further includes: using a two-way matching algorithm to comprehensively evaluate the suitability of talents for positions and the attractiveness of positions to talents, and optionally balancing matching accuracy, recommendation diversity and other business indicators through a multi-objective optimization algorithm.

7. The talent matching method based on artificial intelligence according to claim 5, characterized in that: The feedback and optimization mechanism also includes: based on historical recruitment data and industry benchmark data, intelligently analyzing new job demand information and providing optimization suggestions, as well as dynamically adjusting the skill scarcity, skill-job correlation strength, etc. in the existing knowledge base.

8. An artificial intelligence-based talent matching system, characterized in that: include: The data collection layer is suitable for obtaining candidate resume information, talent demand information of various departments of the company, performance and ability data of current employees, and unstructured data including interview records and work communication records from multiple channels; The data processing layer is connected to the data collection layer and is suitable for processing the acquired data. The data processing layer includes a resume parsing module. The resume parsing module is suitable for converting unstructured resume information into structured talent data using multimodal document understanding technology, named entity recognition technology, and semantic understanding technology. The module is also suitable for cleaning, deduplicating, and standardizing the various types of data, extracting key features, and fusing data from different sources. The intelligent analysis layer, connected to the data processing layer, includes: A talent pool management module, adapted to construct and dynamically maintain an enterprise talent knowledge base based on feedback and continuous learning, said enterprise talent knowledge base integrating the aforementioned processed structured talent data and unstructured communication data; The talent-job matching module is suitable for using a deep learning matching algorithm, combined with information in the enterprise talent knowledge base, to analyze the matching degree between talents and jobs and generate recommendations, and is equipped with a self-optimization mechanism based on system operation results and user feedback; A search enhancement generation module, adapted to connect to the enterprise talent knowledge base and employ a search enhancement generation model to respond to queries input through a natural language interactive interface, providing intelligent question answering and decision support; and a feedback and optimization module, coupled to the talent pool management module and the talent-position matching module, for executing the continuous feedback and optimization mechanism steps of claim 1; an application service layer, connected to the intelligent analysis layer, adapted to provide services to users based on analysis results of the intelligent analysis layer, wherein the services include at least a conversational query engine service; The user interaction layer is connected to the application service layer and is suitable for providing an operation interface for users to access the system and interact with the system. The operation interface at least includes a natural language interaction interface.

9. The system according to claim 8, characterized in that The resume parsing module of the data processing layer is configured as follows: Use optical character recognition technology to process resumes in image format to extract text information; Identify the structured areas of a resume using document layout analysis techniques; Use a deep learning-based named entity recognition model to identify and extract predefined key entity information from resume text; Use pre-trained language models to understand the semantics of resume text, extract relationships between entities, and identify implicit information; Build the extracted entities and relationships into a personal knowledge graph and connect it with the industry knowledge graph or skill dictionary to achieve standardized mapping; And, configured to integrate information sources from external authorized channels such as social media or code repositories to form a more comprehensive talent profile.

10. The system according to claim 8, wherein: The talent pool management module of the intelligent analysis layer is configured as follows: Use a distributed storage architecture to store integrated structured talent data and unstructured communication data; Build a multi-dimensional talent profile for talents based on knowledge graph technology, including hard skills, soft skills, experience, educational background, and personality traits, and support dynamic updates and historical version tracking of the talent profile; Talent characteristics are vectorized and stored in a vector database to support efficient retrieval based on semantic similarity; and the system is configured to automatically adjust the information weights or associations in the enterprise talent knowledge base based on the recruitment results, employee performance or user feedback of the system operation, so as to achieve self-learning and dynamic updating of the knowledge base. The dynamic update further includes time series characteristic analysis of talent characteristics to support potential prediction.