A B/S Architecture Based Evaluation System for College Student Employment Planning

In order to implement a college student employment planning evaluation system, the author proposes a method based on B/S architecture. Based on the B/S architecture, develop a highly practical and targeted college student employment planning and evaluation system. The system obtains students' personality characteristics through carefully set evaluation questions, analyzes and categorizes them, and proposes targeted employment guidance planning plans and suggestions for each student based on mastering their personality characteristics. Customer satisfaction data and big data model distribution for college student work, using big data to match the high level of university student job satisfaction, only data integration, creating relative interest in student work within organizational rules, to enhance and consolidate the portfolio of major interests offered to college career students. Adopting an Apriori Algorithm for Adjusting the Interest Content of College Student Job Recommendations and Improving College Student Job Internship Recommendations by Fuzzy Adaptive Optimization. The results of the study showed that college students were more accurate in using the authorship model when validating their work. In this way, it is possible to better match the interesting characteristics of students and improve the level of work. This means that college students will find better jobs that match their interests and strengths when choosing a major, and thus increase their career success. Prospects for applying this model to college students' job satisfaction are good, and college students' job satisfaction levels have been proven to be better.