## 1 Introduction  
With the development of information technology, many higher education institutions have established information management and monitoring systems [1]. Therefore, timely and effective mining of students' behavioral characteristics has become particularly important. Student consumption data constitutes a vital component of university student datasets. As a branch of behavioral research in higher education, the analysis of student consumption behavior not only explores students' dietary patterns and consumption levels but also provides references for evaluating financial aid. However, traditional campus management concepts and data analysis methods can no longer meet the growing demand for data processing. How to effectively manage and share campus data, optimize student management using data mining approaches, and provide students with clearer and more detailed data services has become challenges facing today's campus service systems. There have been preliminary studies on student consumption utilizing data mining technology both domestically and internationally. [PERSON] employed the k-means clustering algorithm to analyze student consumption data based on two indicators: total consumption amount and average consumption amount. This study also explored the association between student behavior and academic performance [2]. [PERSON] disaggregated the data, mined student consumption patterns, and analyzed the current status of canteen operations [3]. [PERSON] conducted behavioral profiling of college students using various data from universities, providing a comprehensive description of college students' information from multiple dimensions. Through data mining techniques, a cluster analysis of these behavioral profiles was performed to uncover potential patterns in students' academic and social lives [4]. [PERSON] analyzed students' \"One Card\" consumption records to assess their spending levels, subsequently employing neural network-based data mining methods to identify at-risk students [5]. [PERSON] utilized the forward and backward sequential pattern mining algorithm, \"NegI-NSP,\" to analyze students' consumption data, establishing a correlation between consumption patterns and academic performance to examine their relationship [6]. [PERSON]. [PERSON] proposed an early warning system for college student behavior using the Hadoop open-source platform [7]. Traditional clustering analysis, specifically the k-means algorithm, has been used to mine large datasets on campus, studying students' behavioral characteristics and patterns [8; 9; 10; 11]. Most of the aforementioned studies have focused on analyzing student consumption behavior through clustering techniques [12; 13; 14]. Clustering algorithms are a crucial aspect of data mining [15]. The goal of clustering is to group objects in such a way that those within the same class exhibit greater similarity, while objects in different classes show greater dissimilarity. Common clustering methods include density-based [16], model-based [17], hierarchical [18], and partitioning clustering [19]. These algorithms have applications across various fields. For instance, evolutionary clustering algorithms like iECA* have proven effective for grouping medical diseases [20]. To demonstrate how rider expertise affects postural coordination, [PERSON] proposed a framework for automatically analyzing athlete behavior using cluster analysis [21]. [PERSON] introduced a centrality-clustering method called UICPC, enhancing its effectiveness in identifying cellular groupings [22]. [PERSON] developed a new version of spectral clustering named text-associated DeepWalk-Spectral Clustering (TADW-SC) for attributed networks, ensuring structural cohesiveness and attribute homogeneity among identified protein complexes [23]. [PERSON] proposed a novel method for predicting and classifying ventricular arrhythmias (VA), particularly fatal VA, before they occur [24]. Some of the studies referenced above utilized supervised machine learning methods.