Urban river water resources are important ecological resources. In recent years, the continuous development of urban industry has led to the increasingly prominent problem of river water pollution. The traditional sampling and testing methods have high precision, but it is time-consuming and laborious. This study proposes a rapid identification of water quality types based on the second-order differential fluctuation index of the spectrum. The method can realize rapid displacement of water quality types in urban rivers. The method first uses the spectral difference calculation to obtain the second-order differential curve of the spectrum, and smoothes the curve to eliminate noise and other disturbances; then uses the sliding window to extract the local maximum and minimum values on the curve, and sets the minimum distance threshold to gradually remove The extreme false point is obtained by using the cubic spline interpolation method to obtain the double envelope of the second-order differential curve of the spectrum. Finally, the fluctuation index curve of the second-order differential of the spectrum is calculated by using the upper and lower envelopes. After analyzing the fluctuation curves of various samples, it is found that the fluctuation indexes of various water bodies are different at 720~740, 750~770 and 820~840 nm, and then the average fluctuations of various water bodies in these three bands are counted. Statistical characteristics such as the mean value and standard deviation of the index show that the average fluctuation index has a positive correlation with the water quality level. The higher the water quality level, the worse the water quality condition and the larger the average fluctuation index. In order to verify the second-order differential fluctuation index of the spectrum, it can be used for the rapid identification of urban river water quality. The water body spectral samples are randomly divided into training sets and test sets, and LSSVM is used to construct the water quality type, identification model. The average fluctuation index is used as the character input. After testing, the average recognition accuracy of each type of sample is 80.65%, and the recognition accuracy of no more than one type exceeds 95%. The high-spectral identification method of river water quality based on spectral second-order differential fluctuation index proposed by this study has high recognition accuracy and can be used as an auxiliary technical means for rapid detection of urban river water quality types.