Entrepreneurship College, Zhejiang University of Finance & Economics Dongfang College, Haining, Zhejiang 314408, China.
Seoul School of Integrated Sciences & Technologies, Seoul 03767, Republic of Korea.
Comput Intell Neurosci. 2022 Jul 9;2022:3467773. doi: 10.1155/2022/3467773. eCollection 2022.
In order to enhance the competitiveness of enterprises, how to evaluate and enhance the competitiveness of B2B e-commerce enterprises and promote the orderly and healthy development of B2B e-commerce industry are discussed. This paper puts forward the research on the innovation of platform economic business model driven by BP neural network and artificial intelligence technology. BP neural network is used to study and evaluate the competitiveness of B2B e-commerce companies. According to the B2B e-commerce company competitiveness theory and BP neural network algorithm, combined with BP neural network and B2B e-commerce company competitiveness evaluation index system, a BP neural network model is designed to analyze the competitiveness of B2B e-commerce enterprises. Determine the expected value of network samples, select 1 method to determine the subjective weight, and select entropy weight method to determine the objective weight. With the help of the function in the MATLAB neural network toolbox, the neural network is trained. The results show that when the training times reach 3297 times, the sample mean square error is 9.9869 - 06, and the training network reaches convergence. The samples of three enterprises test the trained neural network and input the data of three test samples into the trained BP neural network, and the output results are 0.1531, 0.1371, and 0.1557, respectively. The network model constructed in this paper is effectively close to the training samples. The established BP neural network has good performance and can be used to evaluate the competitiveness of B2B e-commerce companies. Accelerate technological change and realize innovation. Technological capability is the inexhaustible driving force for the development of enterprises. Only with the innovation of keeping pace with the times can application-oriented e-commerce enterprises meet the needs of customers and the market, form the difference between goods or services, and then enable enterprises to win more customers and market share.
为了增强企业竞争力,探讨如何评价和提升 B2B 电子商务企业的竞争力,促进 B2B 电子商务行业的有序健康发展。本文提出了基于 BP 神经网络和人工智能技术的平台经济商业模式创新研究。利用 BP 神经网络研究和评价 B2B 电子商务企业的竞争力。根据 B2B 电子商务公司竞争力理论和 BP 神经网络算法,结合 BP 神经网络和 B2B 电子商务公司竞争力评价指标体系,设计了 BP 神经网络模型,分析 B2B 电子商务企业的竞争力。确定网络样本的期望价值,选择 1 种方法确定主观权重,选择熵权法确定客观权重。借助 MATLAB 神经网络工具箱中的函数对神经网络进行训练。结果表明,当训练次数达到 3297 次时,样本均方误差为 9.9869e-06,训练网络达到收敛。3 家企业的样本测试训练后的神经网络,将 3 个测试样本的数据输入训练后的 BP 神经网络,输出结果分别为 0.1531、0.1371 和 0.1557。本文构建的网络模型有效地接近训练样本。所建立的 BP 神经网络性能良好,可用于评价 B2B 电子商务企业的竞争力。加快技术变革,实现创新。技术能力是企业发展的不竭动力。只有与时俱进的创新,应用型电子商务企业才能满足客户和市场的需求,形成商品或服务的差异化,从而使企业赢得更多的客户和市场份额。