Comparative study of the effects of simple and complex neural network models on enterprise performance

Abstract

Overseas studies have shown that complex neural network models perform better in enterprise performance prediction and can accurately capture the complex relationship of data, but there are long training time, overfitting risk, and limited practical application. Research has gradually focused on balancing model complexity and practicality, while the domestic discussion of the difference in the impact of the two is less, mostly focusing on application validation. In specific industries (e.g., traditional manufacturing), simple models have met the demand due to their high efficiency and transparency; however, in high-dimensional and large-scale data scenarios (e.g., science and technology finance), complex models have significant advantages. In the future, it is necessary to combine the industry characteristics and data scale, deepen the model applicability research, and build a differentiated application framework to optimize the enterprise technology selection and resource allocation efficiency.