In this paper, a simple Bayesian distribution regression learning framework based on probability multi-scale kernel (RMK-BDR) is proposed to study the variation of offshore surface water temperature characteristics and the physical recovery of sports training based on Bayesian regression, which can solve the distribution regression problem of complex data. In the first stage of distributed regression learning, the average embedded estimator of the limited dimension of the same dimension is introduced. Through this method, the number of optimal center points can be adaptively selected according to the sample set, so as to avoid the influence on the sample size of inconsistent regression modeling. Climate change is the most important environmental problem in today’s society. The East China Sea and its adjacent Northwest Pacific have a very important impact on China’s climate. In recent years, it has gradually evolved into an area of international concern. The ocean can transport the heat storage to the atmosphere through the heat exchange between the ocean and the air, promote the movement of the atmosphere through the heat change, and affect the atmospheric cycle. Therefore, it is very important to study the annual and decades’ temporal and spatial variation of sea water temperature around China. At the same time, with the rapid development of basketball, the physical requirements of athletes are also higher and higher. There are many ways to improve the physical strength of basketball players. In addition to scientific training, nutritional support is also an important part. However, for a long time, the lack of understanding of nutritional support and the lack of relevant theoretical support have made nutritional support not fully applied in the basketball field.