Research on the prototype of the university job position matching system based on spark distributed learning

With the continuous increase in the number of college graduates, how to effectively and accurately match college graduates with positions has become the core task of college employment support. The traditional job matching method relies on manual screening and rule matching, which is inefficient and difficult to cope with the demands of large-scale data processing. This paper proposes a prototype of a university job matching system based on Spark distributed learning. By adopting a distributed computing platform and big data processing technology, the timeliness and accuracy of job matching are improved. By building a distributed computing architecture and combining machine learning and deep learning models, features are extracted from a large amount of data to achieve adaptive matching between positions and the resumes of job applicants. The experimental results show that this system significantly improves the intelligent level of employment services in colleges and universities and provides an innovative solution for college employment.