Professional Curriculum Adjustment and System Optimization Based on Big Data

It is an important task to adjust and optimize the professional curriculum in the field of education. Driven by big data, this article studies the course resource recommendation algorithm based on data mining (DM), aiming at better understanding students' needs and providing them with more accurate course resource recommendation. Firstly, DM technology is used to deeply analyze the learning data and extract students' learning preferences and interests. Then, the effect of this algorithm in practical application is assessed through comparative experiments. Finally, the robustness and scalability of the algorithm are verified by system load test and other methods. Through the above research, it is found that this algorithm has obvious advantages in recommendation effect, practical application and system performance. The algorithm can not only better understand the needs of students, effectively improve the learning effect and learning experience, but also better deal with and handle a large quantity of user requests and data, and provide users with more efficient services. Through in-depth analysis of students' characteristics and needs, the algorithm can help to design a more reasonable professional curriculum system to meet students' diverse needs and future career development direction.