Development of a Career Planning Assistance Platform for College Students Combining Fuzzy Logic and Deep Learning

In the increasingly complex field of career planning, it has become a challenge to provide accurate and personalized advice to individuals. This study proposes a career planning assistance platform that combines microservice architecture, fuzzy logic, and deep learning technology. The platform realizes the automated processing of the whole process from user information collection to intelligent recommendation through a four-layer architecture of user interface layer, business logic layer, data processing layer, and service coordination layer. In particular, we developed a fuzzy logic model to process the fuzzy information submitted by users and innovatively designed a deep learning model with multimodal fusion to capture the complex relationships among individual interests, abilities, and career trends. The experimental results show that compared with the traditional recommender system, the model shows significant improvement in metrics such as accuracy, recall, F1 score, and root-mean-square error. In addition, through case studies, we verified the effectiveness of the model in personalized recommendation and obtained positive feedback from users. The results show that the platform not only improves the intelligence of career planning, but also provides new ideas for future research and development.