Abstract: Firstly, aiming at the problems of  the traditional K-means algorithm’s assumption of feactre equality in  processing high-dimensional heterogeneous data, which led to  the neglect of important features,  high sensitivity of clustering results to the preset number of clusters, and strong dependence on the selection of initial centroids, we proposed an adaptive dynamic feature weighting K-means algorithm (ADFW-K-means), which integrated multiple techniques, such as dynamic feature weighting, 
K-means++-optimized initialization, the elbow rule for cluster number selection, an empty cluster handling mechanism, and an adaptive cluster number adjustment strategy. Secondly, the experiments conducted on the targeted selection graduates dataset of  Jilin University from 2022 to 2024. The experimental results show that compared with traditional clustering algorithms, 
the  ADFW-K-means algorithm achieves significant improvements in three core metrics of  silhouette coefficient, clustering stability, and business interpretability,  effectively overcoming the inherent limitations of traditional methods, significantly enhancing the accuracy and robustness of clustering for complex high-dimensional heterogeneous data.

Key words:  , adaptive cluster number, dynamic feature weighting, K-means algorithm, clustering algorithm