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Related Experiment Video

Updated: Apr 29, 2026

Determination of Aggregate Surface Morphology at the Interfacial Transition Zone ITZ

Determination of Aggregate Surface Morphology at the Interfacial Transition Zone ITZ

Published on: December 16, 2019

Enterprise service user intent prediction based on fast K-means++ fusion algorithm.

Yuanyuan Han¹, Juanjuan Zhai¹, Ping Li¹

¹Institute of Digital Economy and Smart Management, Huanghe Jiaotong University, Jiaozuo, China.

|April 27, 2026

Summary

This study introduces an improved enterprise service user intent prediction model using K-means++ clustering and Stacking ensemble learning. The model enhances prediction accuracy and efficiency for better enterprise data analysis and resource allocation.

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Area of Science:

Data Science
Machine Learning
Artificial Intelligence

Background:

Enterprise service user intent prediction faces challenges with low efficiency and accuracy.
Traditional K-means++ clustering struggles with slow convergence and uneven weight distribution in large datasets.

Purpose of the Study:

To develop a novel enterprise service user intent prediction model.
To improve feature mining efficiency and prediction accuracy.
To enhance enterprise data analysis and service response speed.

Main Methods:

Adaptive weighted grid information entropy optimization for K-means++ clustering.
Stacking ensemble learning with base classifiers like random forest.
Multidimensional feature fusion for enhanced prediction.

Main Results:

Optimized Fast K-means++ achieved superior clustering quality (e.g., 0.92 silhouette coefficient).
FK Stacking model exceeded 0.97 in accuracy, recall, and F1 score in e-commerce scenarios.
Model optimization reduced memory usage by 50% and response time by 82.5%.

Conclusions:

The proposed model offers a lightweight and high-accuracy solution for enterprise user intent prediction.
It effectively addresses limitations in feature mining and prediction accuracy.
The model supports enterprises in optimizing resource allocation and improving service speed.