Network Attribute Analysis and Competitiveness Evaluation of Auto Parts Industry Cluster for e-Commerce Platform

Abstract

AbstractThe automotive parts industry cluster is a complex operating system, and it is no longer possible to use a simple theory to make a scientific and reasonable explanation for the parts industry cluster. In view of this, the study takes the automotive parts industry cluster as the research object, innovatively invokes the theory of organizational ecology and constructs a competitiveness analysis mechanism based on population co-evolution. The study conducts performance analysis and simulation tests on the constructed model, integrating the CS–AHP evaluation method to evaluate the competitiveness of the parts industry cluster. The performance comparison results show that the research method has a minimum training error of 0.0108, when the number of iterations of the system is 69th. This method has a stable loss function value at the fifth iteration, which is a stable convergence state earlier than the GA algorithm, differential evolution and distribution estimation algorithm. The A-level index score of the off-line platform auto parts industry cluster is 0.5174, which is significantly greater than 0.5. The competitive cooperation effect, technological innovation effect and cluster culture scores of the e-commerce platform are 0.5885, 0.6899 and 0.5329, respectively, which are significantly higher than those of the off-line. The above results show that the accuracy of the research algorithm is higher than that of the off-line industry. The above results show that the accuracy of the research algorithm is better, which can make a reasonable scientific basis for the auto parts industry and carry out competitiveness evaluation. To a certain extent, it can provide technical reference for the future development of the auto field industry.