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Title: Automatic classification and mining algorithm for massive big data based on machine learning

Authors: Feng Qin; Qiang Wang

Addresses: Changzhou University Huaide College, Jingjiang, 214500, Jiangsu, China ' Changzhou University Huaide College, Jingjiang, 214500, Jiangsu, China

Abstract: In order to improve the accuracy of automatic classification mining of massive big data and reduce the memory consumption of classification mining, a machine learning based algorithm for automatic classification mining of massive big data is proposed. Firstly, the local outlier factor algorithm is used for data cleaning, and a generative adversarial network is introduced to fill in missing values. Secondly, utilising convolutional neural networks to extract massive big data features and inputting them into support vector machine algorithms in the field of machine learning for classification mining. Then, the particle swarm optimisation algorithm is used to improve the support vector machine, while introducing dynamic inertia weights and position update constraints to enhance the particle swarm optimisation algorithm. Finally, use the optimised support vector machine algorithm to implement classification mining. The results indicate that the ARI value of the proposed method is above 0.962, the highest F1 score reaches 0.967, and the highest memory peak is 31.7GB.

Keywords: classification mining; massive big data; generate adversarial networks; support vector machine; SVM; particle swarm optimisation algorithm.

DOI: 10.1504/IJBIDM.2026.154234

International Journal of Business Intelligence and Data Mining, 2026 Vol.28 No.9, pp.155 - 175

Received: 26 Dec 2025
Accepted: 13 Mar 2026
Published online: 17 Jun 2026 *