This article aims to explore the innovation of retail enterprise business models to cope with the challenge of weak industry growth. By constructing a grey wolf optimization AHP store grading evaluation model, the goal of improving single store yield can be achieved. Combining knowledge from multiple disciplines such as marketing and management, this article selects seven reference indicators, including the economic status, population consumption level, and consumption concept of the location of the store, to construct a grey wolf optimization AHP store grading evaluation model. Through empirical analysis of 15 listed retail companies, the Analytic Hierarchy Process (AHP) was used for weight allocation and consistency testing, and the grey wolf optimization algorithm was combined to improve the AHP evaluation process. The empirical analysis results show that there are differences in the AHP evaluation values of different retail enterprises. Some enterprises such as Minsheng Jiale and Hualian Comprehensive Supermarket have higher evaluation values, while Yonghui Supermarket, Xinbai Supermarket and other enterprises have lower evaluation values. The analysis also indicates that business model innovation is influenced by multiple factors at both macro and micro levels. Retail enterprise business model innovation needs to comprehensively consider multiple factors, optimize the store grading evaluation model, implement differentiated competition strategies, and enhance single store sales capabilities. At the same time, enterprises should pay attention to consumer needs and innovate in market segmentation, market positioning, marketing mix, and other aspects to meet the needs of different consumers and achieve sustainable development.
