Title: Bank marketing model based on improved neural network algorithm

Authors: Tongdi Hou; Jie Chen

Addresses: School of Economic and Trade Management, Yancheng Polytechnic College, Yancheng, 224005, China ' School of Information and Security, Yancheng Polytechnic College, Yancheng, 224005, China

Abstract: In commercial banks, traditional marketing methods cannot directly and accurately predict customer needs and preferences, leading to a decline in bank competitiveness. With the progress of big data, deep learning has been applied in many fields. CNN has the characteristics of high-dimensional data and nonlinear data processing. Research using CNN to design marketing models, introducing gravity search algorithm to solve the problem of uncertain network structure selection and overfitting, and using bagging ensemble learning algorithm integration to improve generalisation ability. Due to the uncertainty of network structure in the simulated annealing algorithm, this algorithm was chosen to optimise CNN for comparison. The experiment showed that the CNN MSE optimised by the study was 0.0096, and compared with the comparative model MSE = 0.1021, the similarity between the predicted value and the actual value reached 87%. Therefore, the marketing model based on gravity search algorithm optimisation and bagging integration has good development potential.

Keywords: convolutional neural network; CNN; gravitation search algorithm; GSA; simulated annealing algorithm; bagging integration; MSE evaluation.

DOI: 10.1504/IJCSYSE.2026.151337

International Journal of Computational Systems Engineering, 2026 Vol.10 No.1/2/3/4, pp.69 - 77

Received: 23 Aug 2023
Accepted: 27 Sep 2023
Published online: 26 Jan 2026 *

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