Method and system for realizing automatic employment guidance based on machine learning technology
Through machine learning technology, we collect, process and analyze employment guidance data and generate personalized suggestions, solve the shortcomings of traditional employment guidance methods, and realize efficient and personalized employment guidance services to adapt to changes in the employment market.
Patent Information
- Application Number
- CN202510460850.8
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-04-14
- Publication Date
- 2025-07-22
AI Technical Summary
The traditional employment guidance methods are insufficiently personalized and inefficient, and insufficient data utilization, making it difficult to meet personalized needs and respond quickly to changes in the employment market.
Using machine learning technology, we collect and preprocess career guidance data, extract key features, use algorithms such as deep learning and support vector machines to train models, generate personalized employment suggestions, and optimize the model through real-time interaction and user feedback through online platforms.
Provide personalized and fast employment guidance services to improve targetedness and effectiveness, adapt to changes in the employment market, and use big data analysis and machine learning to update strategies in real time.
Smart Images
Figure CN120355543A_ABST
Abstract
Description
Technical Field
[0001] The present invention relates to the cross - technical field of artificial intelligence and employment guidance, and specifically to a method and system for realizing automated employment guidance based on machine learning technology. Background Technique
[0002] Currently, departments such as universities and vocational training institutions mainly adopt methods such as manual consultation, lectures, and courses in employment guidance. Although these methods help students understand the employment market and career planning to a certain extent, the following problems exist:
[0003] 1. Lack of personalization: Traditional employment guidance methods are difficult to provide personalized suggestions according to the specific situation of each student.
[0004] 2. Low efficiency: Manual consultation and lecture forms are difficult to cover the needs of all students, and the information transmission speed is slow.
[0005] 3. Insufficient data utilization: A large amount of vocational guidance Q&A data is not effectively utilized, lacking systematic data mining and analysis. Summary of the Invention
[0006] The purpose of the present invention is to provide a method and system for realizing automated employment guidance based on machine learning technology to solve the problems raised in the above - mentioned background technique.
[0007] To achieve the above - mentioned purpose, the present invention provides the following technical solution: A method for realizing automated employment guidance based on machine learning technology, including the following steps:
[0008] Collect and pre - process vocational guidance data, where the data includes student consultation records, expert answer texts, and employment market analysis reports;
[0009] Extract student major, interests, employment intentions, and market demand characteristics, and screen key features through a feature selection algorithm;
[0010] Train a model using deep learning, support vector machine, or random forest algorithm, and optimize the model performance through cross - validation and parameter tuning;
[0011] Generate an interpretable intelligent large - model, and output personalized employment suggestions, career path planning, and market dynamic analysis in combination with student individual information;
[0012] Deploy the model to an online platform to support real - time interactive queries, and continuously optimize the model through user feedback and incremental learning.
[0013] Preferably, the data preprocessing step further includes: using natural language processing techniques to clean, de-duplicate, tokenize, and semantically annotate text data, constructing a structured career guidance knowledge graph, and integrating employment market salary data, job demand trends, and industry development reports to form a multi-dimensional dataset.
[0014] Preferably, the feature selection algorithm includes: using the chi-square test based on information gain to screen text features, combining L1 regularization to reduce the dimension of numerical features, and explaining the importance of model features through SHAP values to ensure that the feature engineering conforms to the prior knowledge in the field of career guidance.
[0015] Preferably, the model training process is further defined as: using a Transformer architecture pre-trained language model to capture text semantics, combining a graph neural network (GNN) to process career association relationships, automatically searching the hyperparameter space through Bayesian optimization, and using adversarial validation to improve the robustness of the model.
[0016] Preferably, it also includes system deployment and application, including: constructing a multi-modal interaction interface to support voice / text input, designing a configurable recommendation engine to adapt to the needs of different educational levels, integrating an A / B test framework to evaluate the effectiveness of recommendations, and aggregating multi-source user feedback data through federated learning while ensuring privacy.
[0017] A system for implementing automated career guidance using machine learning techniques, including:
[0018] A multi-source heterogeneous data collection module: used to aggregate student consultation records, expert answer texts, employment market analysis reports, and job demand data;
[0019] A hybrid preprocessing engine: integrating functions of text cleaning, semantic de-duplication, domain dictionary tokenization, and intent annotation to generate a structured career guidance knowledge base;
[0020] A dynamic feature extraction layer: establishing a two-way feature mapping based on student portraits and market dynamics;
[0021] A composite model training framework: supporting joint training of deep learning, ensemble learning, and knowledge graph embedding, with built-in automated parameter search and adversarial sample defense mechanisms;
[0022] A personalized recommendation engine: combining context awareness and multi-objective optimization algorithms to generate three-dimensional recommendations including career path maps, ability gap analysis, and market trend predictions in real time.
[0023] Preferably, the hybrid preprocessing engine further includes: performing fine-grained semantic disambiguation using the BERT-BiLSTM model, and filtering out noise data in combination with a rule engine; constructing a dynamic domain dictionary to automatically update the synonym libraries of industry terms and job names; implementing a hybrid key sentence extraction algorithm based on TF-IDF and TextRank to ensure information integrity and redundancy control.
[0024] Preferably, the dynamic feature extraction layer includes: a student feature matrix: fusing explicit labels and implicit portraits; a market feature map: integrating recruitment website API data, government labor statistics, and industry white papers to construct a spatio-temporal evolution model; a cross-modal feature alignment mechanism: achieving unified representation learning of text-numerical-graph structures through a self-attention network.
[0025] Preferably, the composite model training framework is further defined as: implementing collaborative training of multi-campus data using a federated learning architecture, and combining differential privacy to protect user sensitive information; designing a multi-task loss function to simultaneously optimize career matching prediction, salary prediction, and career stability assessment; integrating a SHAP-TCN interpretability module to provide a time-series analysis of feature contribution degrees.
[0026] Preferably, the personalized recommendation engine has: multi-granularity recommendation generation: providing three layers of recommendations at the job level, industry level, and competency level; a human-machine collaboration interface: supporting voice interaction and a visual career map, integrating user emotion recognition and dialogue context tracking; a closed-loop optimization mechanism: dynamically adjusting the recommendation strategy through reinforcement learning, and combining an A / B test framework to evaluate long-term user benefits.
[0027] Compared with the prior art, the beneficial effects of the present invention are as follows:
[0028] The method and system for realizing automated career guidance based on machine learning technology proposed by the present invention provide personalized career guidance services according to the specific situation of each student, improving the pertinence and effectiveness of the services; using machine learning algorithms to quickly process and analyze a large amount of data to provide efficient and accurate career guidance suggestions; based on big data analysis and machine learning technology, being able to update and adjust career guidance strategies in real time to adapt to changes in the job market. Brief Description of the Drawings
[0029] Figure 1 It is a flowchart of the method of the present invention. Detailed Embodiments
[0030] In order to clearly and completely describe the objectives, technical solutions of the present invention, and make the advantages more clearly understood, the following further details the embodiments of the present invention with reference to the accompanying drawings. It should be understood that the specific embodiments described herein are some, but not all, embodiments of the present invention, and are only used to explain the embodiments of the present invention, not to limit the embodiments of the present invention. All other embodiments obtained by those of ordinary skill in the art without creative efforts belong to the scope of protection of the present invention.
[0031] Example 1. Please refer to Figure 1 , the present invention provides a technical solution: a method for realizing automated employment guidance based on machine learning technology, including the following steps:
[0032] 1. Data collection and preprocessing
[0033] Collect existing career guidance Q&A databases, including student consultations, career guidance expert answers, employment market analysis reports, etc.
[0034] Preprocess the data, including cleaning, deduplication, word segmentation, annotation, etc., to improve the data quality.
[0035] 2. Feature extraction and selection
[0036] Extract key features in the data, such as student majors, interests, employment intentions, market demands, etc. Through feature selection algorithms, screen out the features that have the most influence on employment guidance.
[0037] 3. Model training and optimization
[0038] Select appropriate machine learning algorithms, such as deep learning, support vector machines, random forests, etc., to train the career guidance Q&A database. Through methods such as cross-validation and grid search, optimize the model parameters and improve the model performance.
[0039] 4. Intelligent large model generation
[0040] Use the trained model to generate an intelligent large model for employment guidance. This model can provide personalized employment suggestions, career planning solutions, and market analysis reports according to the specific situation of students.
[0041] 5. System deployment and application
[0042] Deploy the intelligent large model to an online platform for students to access and query at any time. Through user feedback and continuous learning, continuously optimize and improve the model.
[0043] Example 2. On the basis of Example 1, a system for a method of realizing automated employment guidance based on machine learning technology is proposed, including:
[0044] Multi-source Heterogeneous Data Acquisition Module: Used to aggregate students' consultation records, experts' answer texts, employment market analysis reports, and job demand data;
[0045] Hybrid Preprocessing Engine: Integrates functions of text cleaning, semantic deduplication, domain dictionary word segmentation, and intent annotation to generate a structured career guidance knowledge base; The hybrid preprocessing engine further includes: Using the BERT-BiLSTM model for fine-grained semantic disambiguation, combined with a rule engine to filter out noise data; Constructing a dynamic domain dictionary to automatically update the industry term and job name synonym library; Implementing a hybrid key sentence extraction algorithm based on TF-IDF and TextRank to ensure information integrity and redundancy control.
[0046] Dynamic Feature Extraction Layer: Builds a bidirectional feature mapping based on students' portraits and market dynamics; The dynamic feature extraction layer includes: Student Feature Matrix: Fuses explicit labels and implicit portraits; Market Feature Map: Integrates recruitment website API data, government labor statistics, and industry white papers to construct a spatio-temporal evolution model; Cross-modal Feature Alignment Mechanism: Achieves unified representation learning of text-numerical-graph structures through a self-attention network.
[0047] Composite Model Training Framework: Supports joint training of deep learning, ensemble learning, and knowledge graph embedding, with built-in automated parameter search and adversarial sample defense mechanisms; The composite model training framework is further defined as: Using a federated learning architecture to achieve collaborative training of multi-campus data, combined with differential privacy to protect users' sensitive information; Designing a multi-task loss function to optimize career matching prediction, salary prediction, and career stability assessment simultaneously; Integrating the SHAP-TCN interpretability module to provide a temporal analysis of feature contribution degrees.
[0048] Personalized Recommendation Engine: Combines context awareness and multi-objective optimization algorithms to generate three-dimensional suggestions including career path maps, ability gap analysis, and market trend predictions in real time; The personalized recommendation engine has: Multi-granularity Recommendation Generation: Provides three layers of suggestions at the job level, industry level, and ability level; Human-Machine Collaboration Interface: Supports voice interaction and visual career maps, integrating user emotion recognition and dialogue context tracking; Closed-loop Optimization Mechanism: Dynamically adjusts the recommendation strategy through reinforcement learning, combined with an A / B test framework to evaluate long-term user benefits.
[0049] Although the embodiments of the present invention have been shown and described, for those of ordinary skill in the art, it can be understood that various changes, modifications, substitutions, and variations can be made to these embodiments without departing from the principles and spirit of the present invention. The scope of the present invention is defined by the appended claims and their equivalents.
Claims
1. A method for realizing automated career guidance based on machine learning technology, characterized in that: It includes the following steps: Collect and preprocess career guidance data, including student consultation records, expert answer texts, and employment market analysis reports; Extract students' majors, interests, employment intentions, and market demand characteristics, and screen key features through feature selection algorithms; Train a model using deep learning, support vector machines, or random forest algorithms, and optimize the model performance through cross-validation and parameter tuning; Generate an interpretable intelligent large model, and output personalized employment suggestions, career path planning, and market dynamics analysis in combination with students' individual information; Deploy the model to an online platform to support real-time interactive queries, and continuously optimize the model through user feedback and incremental learning.
2. The method for realizing automated career guidance based on machine learning technology according to claim 1, wherein: The data preprocessing step further includes: using natural language processing techniques to clean, de-duplicate, segment, and semantically annotate text data, constructing a structured career guidance knowledge graph, and integrating employment market salary data, job demand trends, and industry development reports to form a multi-dimensional data set.
3. The method for realizing automated employment guidance based on machine learning technology according to claim 2, characterized in that: The feature selection algorithms include: using the chi-square test based on information gain to screen text features, combining L1 regularization to reduce the dimensionality of numerical features, and explaining the importance of model features through SHAP values to ensure that the feature engineering conforms to the prior knowledge in the field of career guidance.
4. A method for realizing automated employment guidance based on machine learning technology according to claim 3, characterized in that: The model training process is further defined as: using the Transformer architecture to pre-train a language model to capture text semantics, combining the graph neural network GNN to process career association relationships, automatically searching the hyperparameter space through Bayesian optimization, and using adversarial validation to improve the robustness of the model.
5. The method for realizing automated career guidance based on machine learning technology according to claim 4, wherein: It also includes system deployment and application, including: building a multi-modal interaction interface to support voice / text input, designing a configurable recommendation engine to adapt to the needs of different educational levels, integrating an A / B test framework to evaluate the effectiveness of suggestions, and aggregating multi-source user feedback data through federated learning while ensuring privacy.
6. A system for the method of implementing automated employment guidance based on machine learning technology according to claim 5, characterized in that: It includes: Multi-source heterogeneous data collection module: used to aggregate student consultation records, expert answer texts, employment market analysis reports, and job demand data; Hybrid preprocessing engine: integrating functions of text cleaning, semantic de-duplication, domain dictionary segmentation, and intent annotation to generate a structured career guidance knowledge base; Dynamic feature extraction layer: building a two-way feature mapping based on student portraits and market dynamics; Composite model training framework: supporting the joint training of deep learning, ensemble learning, and knowledge graph embedding, with built-in automated parameter search and adversarial sample defense mechanisms; Personalized recommendation engine: combining context awareness and multi-objective optimization algorithms to generate three-dimensional suggestions including career path maps, ability gap analysis, and market trend predictions in real time.
7. A system according to claim 6, wherein: The hybrid preprocessing engine further includes: using the BERT-BiLSTM model for fine-grained semantic disambiguation, combining a rule engine to filter out noise data; constructing a dynamic domain dictionary to automatically update the synonym library of industry terms and job names; implementing a hybrid key sentence extraction algorithm based on TF-IDF and TextRank to ensure information integrity and redundancy control.
8. A system according to claim 7, characterized in that: The dynamic feature extraction layer includes: a student feature matrix that fuses explicit labels and implicit portraits; a market feature map that integrates recruitment website API data, government labor statistics, and industry white papers to construct a spatio-temporal evolution model; a cross-modal feature alignment mechanism that realizes unified representation learning of text-numerical-graph structures through a self-attention network.
9. A system according to claim 8, characterized in that: The composite model training framework is further defined as follows: a federated learning architecture is adopted to achieve collaborative training of multi-campus data, combined with differential privacy to protect user sensitive information; a multi-task loss function is designed to optimize the prediction of career matching degree, salary prediction, and career stability assessment simultaneously; an interpretable SHAP-TCN module is integrated to provide a time-series analysis of feature contribution degrees.
10. A system according to claim 9, characterized in that: The personalized recommendation engine has the following features: multi-granularity recommendation generation that provides three layers of recommendations at the job level, industry level, and ability level; a human-machine collaboration interface that supports voice interaction and a visual career map, integrating user emotion recognition and dialogue context tracking; a closed-loop optimization mechanism that dynamically adjusts the recommendation strategy through reinforcement learning and combines an A / B test framework to evaluate the long-term benefits of users.