College student career path navigation method based on dynamic knowledge graph and post portrait

By combining dynamic knowledge graphs with job profiles, the career path planning tool is updated in real time, solving the problem of the disconnect between market demand and learning paths, realizing personalized and flexible navigation services, and improving system responsiveness and user experience.

CN122242900APending Publication Date: 2026-06-19CHENGDU POLYTECHNIC
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
CHENGDU POLYTECHNIC
Filing Date
2026-05-14
Publication Date
2026-06-19

AI Technical Summary

Technical Problem

Existing career path planning tools struggle to keep pace with market demands in real time, resulting in a disconnect between learning paths and the job market, and failing to provide personalized and dynamically adaptive navigation services.

Method used

The career path navigation method for college students based on dynamic knowledge graphs and job profiles constructs a multi-dimensional career data flow monitoring mechanism by acquiring micro-level user behavior signals and macro-level market demand signals. This enables incremental updates of the dynamic knowledge graph of career skills, partial reconstruction of job profiles and path replanning, and the generation of dynamic navigation instructions.

Benefits of technology

It enables real-time synchronization of the career development knowledge base, improves the system's responsiveness to market changes, enhances the efficiency of computing resource utilization, provides users with planning solutions that combine certainty and flexibility, and reduces the cognitive load of decision-making.

✦ Generated by Eureka AI based on patent content.

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Abstract

This invention belongs to the technical field of data-driven education and career planning. Specifically, it discloses a career path navigation method for college students based on dynamic knowledge graphs and job profiles. The method includes: constructing a node connection graph containing skills, courses, and jobs, and introducing a time decay function and a market event-driven incremental update mechanism for edge weights. Based on the dynamic weights of each edge in the path, a comprehensive score for candidate paths is calculated. Recommendation probabilities are generated using exponential weighting and normalization, and the matching degree between entities is quantified using a vector space cosine similarity algorithm. Finally, the method is visualized through a highlighted topological path or learning roadmap, and supports interactive expansion of branch nodes. This invention achieves automated synchronization with changes in the career environment, improving the logical rigor, market fit, and data visualization effects of career planning path recommendations.
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