Application of Multi-Source Heterogeneous Data Fusion in “One-stop” Student Service Platform

This paper discusses the key role of multi-source heterogeneous data fusion in building a “one-stop” student service platform, aiming to improve the quality of service and personalized experience by effectively integrating student information from different channels. In this paper, Principal Component Analysis (PCA) is used to reduce the dimension of features from different data sources, extract the main features, and select the number of retained principal components to ensure that the retained principal components can maintain most of the information in the original data. Then, the Support Vector Machine (SVM) algorithm was used to construct the model and classify the processed data, and the accurate service matching was carried out according to the needs of students.