Fresh food retail satisfaction and sales forecasts: digital integration for sustainability in Guiyang, China

Introduction Driven by the digital economy, China’s fresh food retail sector is accelerating its transition toward omnichannel integration. However, existing research has largely focused on first-tier cities in eastern China, paying insufficient attention to the heterogeneity of underdeveloped regions in southwestern China, and lacking a systematic analytical framework that integrates multi-source data and multiple methodologies. Methods This study takes six mainstream online fresh food platforms in Guiyang, China (including the local platform ‘Guihuihui’) as its research subjects. By integrating two rounds of stratified sampling questionnaire surveys (1,744 valid responses) with 16,321 online review data points, we constructed a multi-model integrated analytical framework comprising an LDA topic model, an improved intuitive fuzzy TOPSIS method, and the AT-DeepAR deep learning model. This framework was used to systematically investigate the mechanisms underlying the evolution of fresh food retail formats, the factors influencing consumer satisfaction, and market sales forecasting. Results The study found that: (1) transaction costs and service quality are the core drivers of digital transformation in fresh food retail, with consumers placing the greatest emphasis on delivery speed and shopping experience; (2) platform satisfaction rankings are as follows: Duoduo Grocery > Meituan Youxuan > JD NOW > Freshippo > Guihuihui > Dingdong Maicai, with national platforms enjoying significantly higher brand recognition than local platforms; (3) the AT-DeepAR model, incorporating an attention mechanism, can accurately capture the seasonal fluctuations in fresh produce sales, with predicted values closely matching actual figures (within a 95% confidence interval). It forecasts that the average monthly sales of fresh produce platforms in Guiyang will steadily increase to approximately 340 million yuan by 2025. Discussion At the theoretical level, this paper fills a research gap on fresh food e-commerce in southwestern China by establishing an integrated analytical paradigm combining questionnaire data, text mining, and deep learning. At the practical level, it provides empirical evidence to support differentiated competition, transaction cost optimization, and the digital upgrading of the regional fresh food industry for local platforms in Guiyang. It holds significant practical value for promoting the sustainable development of fresh food retail, enhancing the efficiency of urban–rural food distribution, and facilitating the low-carbon transition of supply chains.