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 G1 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 9.9869e − 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.

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This paper aims to study the relationship between green production management and enterprise innovation through empirical analysis of China’s technology-based small and medium-sized enterprises (SMEs). It can promote the improvement of the production management efficiency of enterprises. The rapid development of information technology and the change in social productivity has changed lifestyles in ways that trigger certain challenges in production management, especially in technology-based SMEs. The main issue is the role of leaders and organizational practices. Therefore, this paper designs and improves the structural and operating mechanisms of technology-based SMEs by employing the person fit and evolutionary game models. This paper gathers data from technology-based SMEs of Zhejiang Province, China, by conducting a questionnaire-based survey. The principle of person-environment fit revealed the positive leadership skills of enterprise managers. In addition, the evolutionary game model revealed the re-optimization of SMEs to improve management efficiency through reforming enterprises’ organization, management, and supervision mechanisms. Finally, strengthening collaborative innovation, improving innovation support services, grasping the balanced scale of the system, and boosting the innovation habitat for the healthy and innovative ecosystem of technology-based SMEs are proposed. This paper provides suggestions for policymakers to expand and upgrade management, especially in technology-based enterprises.