In recent years, many cities in the world have been hit by the high temperature heat wave and suffered heavy losses. And many coastal cities have been affected to a certain extent. Through reasonable urban form and architectural design to cope with the heat wave, improving the city’s ability to cope with high temperature has become an important planning and mitigation strategy to adapt to urban high temperature. In this paper, big data is applied to the study of coastal weather characteristics. From the perspective of urban planning and architecture, the interaction mechanism of coastal weather characteristics is discussed by using quantitative analysis methods such as correlation analysis and spatial regression model, which provides an important basis for the planning and urban design of high temperature heat wave. Then, taking urban morphology parameters, land use parameters and LST as variables, Pearson correlation coefficient was calculated by SPSS and GeoDa tools, and a spatial regression model was established to explore the quantitative relationship between coastal weather characteristics and land surface temperature. In the Pearson correlation coefficient, the correlation between vegetation coverage and LST is the largest, showing a negative correlation, with the coefficient of −0.595; 595. In addition to coastal climate types, the correlation between building density and LST is the largest, the coefficient is 0.360, positive correlation, the correlation is the smallest. With the application of big data technology, the tax collection and management mode will develop in the direction of intelligence, efficiency, fairness, and accuracy. Big data technology will provide a new direction for the research of coastal meteorological characteristics and the optimization of urban enterprise finance and taxation. In this paper, through the study of coastal meteorological characteristics of big data, it is applied in the city enterprise financial tax, to promote the enterprise financial tax more standardized.

近年来，世界上许多城市都受到高温热浪的袭击，损失惨重。很多沿海城市都受到了一定的影响。通过合理的城市形态和建筑设计应对热浪，提高城市应对高温的能力已成为适应城市高温的重要规划和减缓策略。本文将大数据应用于沿海天气特征研究。从城市规划与建筑的角度，运用相关分析、空间回归模型等定量分析方法，探讨了沿海天气特征的相互作用机制，为高温热浪的规划和城市设计提供了重要依据。然后，以城市形态参数、土地利用参数和LST为变量，利用SPSS和GeoDa工具计算Pearson相关系数，建立空间回归模型，探讨沿海天气特征与地表温度的定量关系。在Pearson相关系数中，植被覆盖度与LST的相关性最大，呈负相关，系数为-0.595；595. 除沿海气候类型外，建筑密度与LST的相关性最大，系数为0.360，正相关，相关性最小。随着大数据技术的应用，税收征​​管模式将朝着智能化、高效化、公平化、精准化的方向发展。大数据技术将为沿海气象特征研究和城市企业财税优化提供新方向。本文通过对沿海气象大数据特征的研究，将其应用于城市企业财税，推动企业财税更加规范。