## 1 Introduction
Then, such normalization will naturally encourage the local smoothness of the learning function and will hence achieve the purpose of improving the accuracy based on the original model. Lastly, behavior data is massive and mixed, including completely different types of data, such as library records, dormitory entries and exits, consumption at campus locations, book borrowing, and academic achievements [11]. In order to predict students' career choices based on behavior data, data mining approaches such as feature engineering [12] is introduced. Before training our model, we cluster students according to their college information. Afterwards, we establish many new behavior-based representative factors that affect a student's career selection. Inspired by Reference [13], a behavioral entropy index is established to measure the regularity of student behavior. Later, we will discuss in detail how we establish new factors. Specifically, in this paper we collect (mainly through campus smart cards) 4634 students' longitudinal behavioral data spanning almost three years. Based on the statement that clustering the data by feature \"college\" can capture the connections between students, cluster centers are calculated as prototypes for each college [14; 15]. For all instances in a cluster, the cluster center, which is a feature vector, represents their average band. In other words, the cluster center can be a new instance with the average label. Such prototype approach brings multiple important advantages for multiclass learning. The framework of our model is shown in Figure 1. First, four types of behavioral features are generated based on campus behavioral data: mastery of professional skills, behavior regularity, reading interest, and family economic status. Next, the Approach Cluster Centers Based on XGBOOST (ACCBOX) model can be obtained by our prototypical cluster center generation method and a novel regularization item. Finally, the resulting predicted career choice is presented. We use actual career choice data to evaluate our model. In summary, we make the following contributions:  
(1) We collect behavioral data of students at school. Using the location and timestamp of the student's card at the school, the designed algorithm constructs a behavioral entropy index that measures the regularity of student behavior. The behavioral entropy index are used to describe the regularity of students' behavior at school, and the differences in the behavioral patterns of students in different graduating classes are analyzed. Finally, a feature that considers data with and without labels measures the data shift with respect to different years. (2) Based on student behavior, we propose four factors that reflect student traits. Our study shows that these factors are significantly correlated with students' achievements and career choices. (3) We perform an extensive evaluation on a real-world dataset covering over 4634 students. To make our results credible, we perform numerous experiments. A methodology called the ACCBOX model is proposed to model behavioral information of students belonging to different clusters. We verify the effectiveness of our method of career choice prediction through experiments on students' behavior dataset.