Exploring optimal pathways for enterprise procurement management systems based on fast neural modeling and semantic segmentation

Abstract

Corporate procurement management assumes a pivotal role within the contemporary business landscape, yet confronts an array of challenges as markets continue to evolve and globalize. Conventional procurement management systems frequently grapple with issues of inefficiency, resource depletion, and noncompliance, necessitating the exploration of innovative avenues for optimization. This paper delves into the realm of risk mitigation associated with collusion behavior in the administration of intelligent procurement systems, presenting a novel procurement collusion identification model founded on a convolutional neural network (CNN) with reinforcement learning techniques. This framework commences with the application of a CNN and Long Short-Term Memory (LSTM) network for in-depth feature analysis and initial identification of historical procurement data, subsequently leveraging reinforcement learning methodologies to enhance the model's autonomy and intelligence for the purpose of optimization. Throughout the experimental phase, diverse domains of procurement data were meticulously selected for analysis. The empirical findings unequivocally demonstrate the model's proficiency, with an average recognition accuracy of 95.1% across five publicly available datasets. This performance surpasses existing machine learning methodologies employed in contemporary research and common recognition networks, thereby offering a pioneering reference point for the intelligent administration and optimization of future procurement systems.