Research on influencing factors of pharmaceutical e-commerce sales based on web crawler and support vector machine


  • Shengbo Hu



Web crawler, Support vector machine (SVM), Pharmaceutical e-commerce, Sales Analysis.


Based on the current background of the rapid development of the pharmaceutical e-commerce industry, this paper provides an in-depth discussion of the factors affecting pharmaceutical e-commerce sales. With the help of Python crawler technology, the pharmaceutical e-commerce data of Alibaba Health platform is collected with GanMaoLingKeLi(999) as an example. Using support vector machine (SVM), according to the selected characteristic indicators, different schemes are set to predict the sales of pharmaceutical products and determine the influencing factors of pharmaceutical e-commerce sales. The results show that comment characteristics, transaction characteristics, shop characteristics, service characteristics and product characteristics all have different degrees of influence on product sales. Among them, the influence of comment characteristics is the most significant. Based on these results, merchants should attach great importance to the construction of review content and improve the quality of pharmaceutical services to attract and retain consumers. The government should promote the pharmaceutical e-commerce industry to improve service quality and encourage technological innovation. Third-party platforms should optimise website design and strengthen brand image through live broadcasting and other means.


Download data is not yet available.


National Development and Reform Commission. Action Plan for Promoting High-Quality Development of Health Industry (2019-2022): Development and Reform Society No.1427 [EB/OL]. (2019-9-30) [2024-4-1].

iiMedia Consulting. 2023-2024 Global and Chinese pharmaceutical e-commerce market and development trend research report [DB/OL]. (2023-10-12) [2024-4-1].

Zhou Yutao. ' Fourth Terminal ' led the industry 2022 ~ 2023 China pharmacy pharmaceutical e-commerce development report [J]. China pharmacy, 2023, (04) : 103-111.555

Dupuits, FMHM.The Effects of the Internet on Pharmaceutical Consumers and Providers[J].Disease-Manage-Health-Outcomes,2002,10(11),679–691.

Orizio,G.,et al.Cyber drugs:A Cross-Sectional Study of Online Pharmacies Characteristics[J].The European Journal of Public Health,2009,19(4),375–377.

Chen Debao. Problems and improvement measures in the development of pharmaceutical e-commerce in China [J].Foreign economic and trade practice, 2016, ( 01 ) : 38-40.

.Zhang Xiaheng.Construction and development path of pharmaceutical e-commerce ecosystem under ' Internet + ' [J].Contemporary Economic Management, 2016,38 ( 11 ) : 26-29.DOI : 10.13253 / j.cnki.ddjjgl.2016.11.005.

Huang Zhe, Li Hui. Influencing factors of online reviews of online pharmacies based on big data [J].Journal of Shenyang Pharmaceutical University, 2016,33 ( 10 ) : 833-838.DOI : 10.14066 / j.cnki.cn21-1349 / r.2016.10.013

.Zhao, Xiangqi; Gao, Lixiang; Huang, Zhe.Customer satisfaction evaluation for drugs: A research based on online reviews and PROMETHEE-II method[J].Plos One,2023,18(6):0283340.

Luo, Yumei; Li, Yuwei; Ye, Qiongwei.Impacts of Online Additional Reviews on the Sales Volume of Cross-Border Pharmaceutical E-Commerce Platforms[J].Journal Of Global Information Technology Management,2022,25(1),83-101.

Yan, Ma Jing; KANG, Taewon.A Study on the Effects of Service Quality of Pharmaceutical E-commerce on Customer Satisfaction -Mediating Effect of Perceived Value-[J].Korean-Chinese Social Science Studies,2021,19(3),185-205.

Wu, Jianyun; Dong, Mingqiu.Research on customer satisfaction of pharmaceutical e-commerce logistics service under service encounter theory[J].Electronic Commerce Research And Applications,2023,58:101246.

Zou Yueqing, Fan Mengyuan, Xu Xiaoyi, et al. ' Internet + ' under the background of medical e-commerce consumer group characteristics positioning and development strategy research-based on a sample survey of permanent residents in Wuhan [J].Modern Business, 2018, (25) : 26-28.DOI : 10.14097 / j.cnki.5392 / 2018.25.007.

He Yufang, Ma Xinyu, Cui Yanyin, et al. Research on customer satisfaction of pharmaceutical e-commerce platform based on Chinese customer satisfaction index model [J]. China Pharmaceutical, 2023,32 (04) : 1-6.

Jiang Feng, Zhang Wenya. Application of machine learning methods in economic research [J]. Statistics and Decision Making, 2022,38 (04): 43-49.DOI: 10.13546 / j.cnki.tjyjc.2022.04.008.

Chen, Qisong; Wu, Yun; Chen, Xiaowei.Research on Customers Demand Forecasting for E-business Web Site Based on LS-SVM[J].Proceedings Of The International Symposium On Electronic Commerce And Security,2008,66-70.

Tang Yifei. Dynamic demand forecasting of fresh agricultural products in e-commerce environment [D].Nanjing University, 2014.

Ma Jiayu. Research on the prediction of fresh agricultural products demand of a farm e-commerce based on combination model [D]. Shandong University of Science and Technology, 2020.DOI : 10.27275 / d.cnki.gsdku.2020.000887.

Wang Gang; Yin Fenxia.A Method of Recommendation Based on Affection Semantic in E-Business[J].Software Engineering And Knowledge Engineering: Theory And Practice, Vol 1,2012,114,369.

Chen Yanfang. Online product review credibility classification model based on DDAG-SVM [J].Information theory and practice, 2017,40 (07) : 132-137.DOI : 10.16353 / j.cnki.1000-7490.2017.07.024.

Zhang Wenyu, Yue Kun, Zhang Binbin. Detection of fake reviewers in e-commerce based on D-S evidence theory [J].Microcomputer system, 2018,39 (11) : 2428-2435.

Sasi A, Deep A, Kumar K, et al. Machine Intelligence and Smart Systems[C].Singapore.Springer,2021:287-296.

National Bureau of Data. " Data Elements × 3-Year Action Plan (2024-2026) " : Country Number Policy No.11 [EB/OL]. (2024-1-5) [2024-3-23].

Shi Chengyu, Chen Guaiheng, Wang Yan, etc. Research on logistics service strategy of fresh e-commerce supply chain from the perspective of big data [J].Agricultural technology and economy, 2023, (10) : 129-144.DOI : 10.13246 / j.cnki.jae.2023.10.005.

Yang Shenggang, Xie Jinyuan, Cheng Cheng. Cross-border e-commerce, supply chain optimization and enterprise internationalization - empirical evidence based on big data text analysis [J]. International trade issues, 2023, (10) : 1-18.DOI : 10.13510 / j.cnki.jit.2023.10.001.

Zhang Yanliang, Dai Peipei. Multivariate SVR demand forecasting for fresh products-Extraction of customer perception factors based on online reviews [J].Journal of China Agricultural University, 2022,27 (07) : 275-282.

Zhou Wei. The impact of e-commerce sinking on the circulation of agricultural products from the perspective of factor endowment differences [J].Business Economics Research, 2022, (01) : 89-92.

Cortes, C., Vapnik, V. Support-vector network [J]. Machine Learning, 1995( 20) : 1-25.

Gu Shen, Wang Shujuan. Research on carbon financial risk early warning model based on SVM [J].East China Economic Management, 2019,33 (03) : 179-184.DOI : 10.19629 / j.cnki.34-1014 / f.171220004.

Cernadas E., Amorim D. Do we need hundreds of classifiers to solve real world classification problems? [J]. Journal of Machine Learning Research, 2014( 1) : 3133-3181.

Zhu Wenjing, Liang Miao. Based on the 5G era, thinking about the pharmaceutical service mode of online pharmacies in China [J].Exploration of rational drug use in China, 2021,18 (06) : 10-14.

Zhou Yutao.B2C to the left, O2O to the right 2021 Report on the development of pharmaceutical e-commerce in Chinese pharmacies [J].Chinese pharmacies, 2022 ( 04 ) : 94-99.




How to Cite

“Research on influencing factors of pharmaceutical e-commerce sales based on web crawler and support vector machine” (2024) Transactions on Computer Science and Intelligent Systems Research, 4, pp. 48–59. doi:10.62051/1609wx31.

Similar Articles

1-10 of 46

You may also start an advanced similarity search for this article.