Open AccessService quality evaluation of fresh agricultural products cold chain logistics based on principal component and neural network
Author(s) -
Mian Fu,
Dandan Wang
Publication year - 2020
Publication title -
iop conference series. earth and environmental science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.179
H-Index - 26
eISSN - 1755-1307
pISSN - 1755-1315
DOI - 10.1088/1755-1315/585/1/012103
Subject(s) - cold chain , connotation , principal component analysis , quality (philosophy) , service (business) , agriculture , business , artificial neural network , service quality , component (thermodynamics) , sample (material) , environmental economics , computer science , operations management , manufacturing engineering , marketing , engineering , artificial intelligence , food science , chemistry , ecology , philosophy , linguistics , physics , epistemology , chromatography , economics , biology , thermodynamics
With the rapid development of fresh e-commerce, the cold chain logistics service quality of fresh agricultural products has attracted the attention of businesses and customers. Starting from the connotation of cold chain logistics service quality, the evaluation index system of agricultural products cold chain logistics service quality with 13 influencing factors was established. Considering that too many dimensions of input data and strong correlation among indexes will affect the training of neural network, this paper proposes to build a service quality evaluation model of cold chain logistics of fresh agricultural products by combining principal component analysis with BP neural network. Through the actual data verification of an e-commerce logistics enterprise, the evaluation accuracy rate is significantly improved, which provides an effective method for the service quality evaluation of fresh agricultural products cold chain logistics.