A Layered After-sale Quality Evaluation Model Based on Improved Artificial Neural Network

Abstract

In this paper, a model of quality evaluation of after-sale service based on improved artificial neural network is proposed. This model applies an improved analytic hierarchy process to determine the weight of the evaluation index system, and then employs the artificial neural network to evaluate the after-sale service quality. Aiming at the problem that the classical BP neural network has slow convergence speed and low learning efficiency, this paper uses an accelerated BP neural network learning algorithm based on momentum constants. Compared with the classical BP neural network, the accelerated neural network in this paper has higher learning efficiency. Finally, through the evaluation experiment of the automobile service quality, the evaluation model of service quality can be used to evaluate the quality of the After-sale service, which provides the scientific and effective guidance for the evaluation of the service quality of the automobile.