Design and implementation of career planning system based on machine learning

To better cater to the needs of job seekers and provide enhanced career development directions, this study proposes a novel employment guidance and career planning system integrating machine learning algorithms, along with the creation of a career matchmaking framework. Firstly, leveraging the RippleNet algorithm model, we construct a knowledge graph-based career planning recommendation system. The system-building process involves investigating key technologies in data processing and recommendation algorithms. Subsequently, through the implementation of database security design, we ensure the information security of users within the system, thereby maintaining the efficient operation of the system. The results indicate that the system is well-functioning, exhibits stable performance, and is capable of meeting the fundamental requirements of a career planning system.