The analysis of temporal and spatial characteristics is of great significance for comprehensively understanding water quality variation. However, the existing temporal and spatial water quality analysis methods still have some shortcomings, such as indistinguishable order of water quality variation, the unclear extreme value of water quality variation, and unclear water quality evaluation characteristic values. To analyze the temporal and spatial characteristics information of water quality more clearly, this study took Qinhuai River as a research object and referred to the empirical frequency method of engineering hydrology. A "hydrological frequency-water quality" fitting curve was established, which was used to explore the difference between high/low activity areas in different periods and wet/dry seasons in different reaches. The "hydrological frequency-water quality" fitting curve method was compared with the traditional boxplot method. The results showed that compared with the boxplot, the "hydrological frequency-water quality" fitting curve could quantify key water quality evaluation points and characteristic value information, making the temporal and spatial variation process of water quality clearer. The best form of the "hydrological frequency-water quality" fitting curve in time was the line number curve, and the water quality concentration generally did not change abruptly. The best form of the "hydrological frequency-water quality" fitting curve in space was an exponential curve, and the water quality concentration had sudden increases were more likely. The concentration of nitrogen and phosphorus in the high activity area was higher than that in the low activity area during each period, and the concentration of nitrogen and phosphorus in the wet season of each river section was lower than that in the dry season. The analysis process of this method was simple and convenient, the results were intuitive and orderly, and the water quality information could be automatically reflected by statistical analysis. It could be used as an optimal method to study the temporal and spatial characteristics of river water quality when the sampling points, sampling time and sampling frequency are typical.