Bacterial foodborne diseases caused by pose persistent challenges to coastal cities in China. In this study, we employed multiple logistic regression analysis and distributed lag non-linear models (DLNM) to investigate the epidemiological characteristics and associated risk factors of vibriosis in the metropolitan area of Hangzhou from 2014 to 2018. Analysis of foodborne cases indicated that certain demographics and occupational factors, including age between 16 and 44 years; houseworkers or unemployed individuals; preference for aquatic and meat products; and dining in collective canteens or catering services contribute to an increased likelihood of infection. Moreover, a higher per capita GDP and exposure to high temperatures were identified as risk factors for vibriosis. This study highlights the significance of the daily mean temperature as a meteorological factor influencing infection, with varying lag effects observed depending on temperature conditions. At low temperatures, the risk of infection occurs after a lag of 21 days, whereas at high temperatures, the risk is highest on the same day, while the second infection risk period occurs after a lag of 21 days. These findings provide a spatiotemporal perspective of the risk analysis of foodborne diseases, with a daily timescale and street spatial scale, which contributes to the development of public health strategies and food safety protocols in coastal cities.