Evaluation Method for Competitiveness of Agricultural Product E-commerce Platforms based on Convolutional Neural Networks

With the rapid development of e-commerce, the e-commerce platform of agricultural products plays an important role in promoting the circulation of agricultural products and improving farmers' income. The competition between platforms is increasingly fierce, how to evaluate the competitiveness of agricultural products e-commerce platform scientifically and accurately has become the focus of the industry and academia. The research first analyzed the development status and competition situation of the e-commerce platform of agricultural products, then introduced the basic principles and advantages of CNN in detail, and discussed its applicability in the competitiveness evaluation of the e-commerce platform of agricultural products. On this basis, a competitiveness evaluation model based on CNN was constructed, which could automatically extract key features from the sales data of the platform. And by learning the rules of historical data to predict the future competitiveness trend. The results show that the evaluation model based on CNN performs well in predicting the return rate of customers and evaluating the service quality of the platform, and can accurately reflect the competitiveness of the platform. Compared with other traditional evaluation methods, this model has higher accuracy and stability, and can provide a more scientific and objective competitiveness evaluation for e-commerce platforms. The CNN-based competitiveness evaluation method proposed in this study provides a new and effective evaluation tool for the e-commerce platform of agricultural products. This method not only has theoretical value, but also has wide application prospect.