Approach to constructing student portrait based on multilevel labeling system

Along with the development of new-generation technologies such as Big Data and Artificial Intelligence, education has attracted much attention as an important practice area for cutting-edge technologies. In this context, the concept of student portrait has emerged. Through in-depth analysis of student profiles, it can help educational institutions implement differentiated teaching more effectively and improve the quality of education. However, in the process of applying student portrait, some problems have emerged: on the one hand, although data mining algorithms are already very mature, they are less used in the field of student portrait and their accuracy is low. On the other hand, different educational institutions and platforms build different student portrait labels based on their own needs and understanding, which lacks universality and interoperability. Aiming at the above problems, we take the construction of student portrait labeling system as the starting point, and construct the student portrait model through the improved K-prototype algorithm, and finally, the experiments show that the constructed student portrait can accurately describe the students, and to a certain extent, it provides data support for the decision-making of the school education administrators, and reduces the cognitive burden of a large amount of data.