Research on Automobile Aftermarket Parts Pricing Model Based on Support Vector Machine

Authors

  • Tengjian Yang
  • Chao Zhang
  • Peipei Zhu
  • Fan Zhang

DOI:

https://doi.org/10.54097/v65n0n61

Keywords:

Automobile after-sale parts; Pricing model; Support vector machine.

Abstract

 In this study, a pricing model based on support vector machine is proposed to solve the problem of auto aftermarket parts pricing. By collecting and analyzing multi-source heterogeneous data, a "1+4+8" pricing index system was constructed, which includes parts cost, market demand and supply, brand and quality, material and performance. Support vector machine (SVM) is used to establish the pricing model of automobile aftermarket parts. The experimental results show that the pricing model based on support vector machine can effectively improve the pricing accuracy and provide data support for enterprises to formulate reasonable pricing strategies. This study provides a new idea and method for the pricing of automotive aftermarket parts, which is of great significance to enhance the competitiveness of enterprises.

Downloads

Download data is not yet available.

References

[1] Wu Xin. Research on Multi-value chain Auto Parts demand Forecast based on Graph Neural Network [D]. Sichuan university, 2023. DOI: 10.27342 /, dc nki. Gscdu. 2023.000580.

[2] Li Yuqing. Research and System Implementation of After-sale Parts Inventory Decision based on Deep Reinforcement Learning [D]. Southwest jiaotong university, 2023. DOI: 10.27414 /, dc nki. Gxnju. 2023.000291.

[3] YAN Weijun. Research and Realization of Supporting Auto Parts sales forecasting method [D]. University of Electronic Science and Technology of China, 2019.

[4] Ling Ning, Guo Jin. Cleaning and visualization of meteorological big data over one million based on Python [J]. Modern Information Technology, 2025, 9 (01): 100-103+109. DOI:10.19850/j.cnki.2096-4706.2025.01.020.

[5] Fang Li-Zhen, Cao Xin-Yu. Intelligent data cleaning and pretreatment algorithm of the information system in colleges and universities study [J]. Computer programming skills and maintenance, 2025, (01) : 115-117. DOI:10.16184/j.cnki.com.prg. 2025.01.003.

[6] FENG Ziang, SHAO Jiayu, Zhang Ning. Research on Flame recognition Algorithm based on Support Vector Machine [J]. Industrial Control Computer, 2025, 38 (01): 79-81.

[7] WANG Rongchao, DUAN Hongguang, LIU Tingqin, et al. SSS sequence detection model based on Residual Convolutional neural Networks-support vector Machines [J/OL]. Radio communication technology, 1-9 [2025-02-11]. http://kns.cnki.net/kcms/detail/13.1099.TN.20250114.1846.006.html.

Downloads

Published

21-02-2025

Issue

Section

Articles

How to Cite

Yang, T., Zhang, C., Zhu, P., & Zhang, F. (2025). Research on Automobile Aftermarket Parts Pricing Model Based on Support Vector Machine. Frontiers in Business, Economics and Management, 18(2), 166-170. https://doi.org/10.54097/v65n0n61