With the development of science and technology, the volume of hydrological information data has increased tremendously, how to make full use of these large-scale data to support decision-making is a big problem for scientists at present. Traditional water ecological carrying capacity analysis and calculation are complex and diverse, involving various types of data, with unsatisfied expansion, and focus on theoretical research and analysis. This work studies historical data, analyzes the factors affecting water ecological carrying capacity, divides the data into three layers, and proposes an analysis model of water Ecological Carrying Capacity based on Big Data (ECCBD). HDFS distributed file system of Hadoop cluster is used to implement the backup and storage of water ecological data, and MapReduce is used to implement the parallel computation of massive water ecological data. By comparing the output value with the water ecological carrying capacity, determining whether the water resources are surplus or deficit, the method and model proposed in this study can effectively analyze the current status of the aquatic environment from three different index layers: pressure, bearing capacity, and elasticity, it is of great significance to provide a basis for water ecological protection.