Research on Key Technologies of Talent Portrait System Based on Cluster Analysis

Abstract

With the rapid digital transformation of human resources, precise talent management has become a core organizational capability. Traditional assessment methods, limited by subjectivity and single-dimensional evaluation, can no longer meet modern talent management needs. Current clustering approaches face challenges such as high-dimensional sparse data, low efficiency and local optima in algorithms like K-means and original Bi-Kmeans, as well as insufficiently specialized talent tag systems that hinder accurate job matching. This study therefore designs and implements a cluster-analysis-based talent profiling system to improve processing of high-dimensional sparse data and the interpretability of clustering results. Using the Oracle database from Jiangxi Province’s Talent Dynamic Management System, datasets of 500, 2,000, and 5,000 records were constructed, each containing 68 feature dimensions (basic information, TF-IDF keywords, LDA topics). The Bi-Kmeans algorithm was improved by integrating Kmeans++ centroid initialization and KD-tree fast nearest-neighbor search, reducing time complexity from O(t·n·d) to O(t·logn·d). A professional talent-label system was built using HanLP segmentation, TF-IDF and LDA, with KNN for label matching. An integrated system covering data cleaning, feature extraction, portrait construction, clustering, and system management was developed. Experiments on the 5,000-record dataset show that the improved Bi-Kmeans achieves 81% clustering purity (20% higher than K-means, 14% higher than original Bi-Kmeans), ARI of 0.75, and 41% faster runtime, while performance variance across 10 runs stays under 5%. The post-processing missing-data rate is under 3%, and KNN label-matching accuracy reaches 92%. Overall, the system operates stably, meets functional requirements, enhances clustering efficiency and stability, enriches the talent-tag dimensions, and provides both theoretical innovation and strong practical value for precise talent management in Jiangxi Province.