Total organic carbon is an index to evaluate the organic pollution of water quality based on carbon content, which can reflect the degree of water pollution. Currently, the detection of total organic carbon in surface water mostly adopts the laboratory analysis method after field sampling. This method has the disadvantages of being time-consuming and laborious, complex operation, secondary chemical pollution, etc. UV-Vis spectroscopy has the advantages of environmental protection, simple operation and real-time on-line in-situ detection. It has a good application prospect in detecting total organic carbon in surface water. The interval partial least squares regression method based on the adaboost algorithm (Ada-iPLSR) is adopted. In this method, the total organic carbon absorption spectrum band is divided into several sub-intervals. The training sample weight is initialized. The partial least squares regression model is established in each sub-interval in turn, the weight coefficient of the prediction result of the sub-interval is calculated according to the prediction error rate of the sub-interval model, and the training sample weight of the next sub-interval is updated. Finally, the prediction results of each sub-interval model are linearly weighted to obtain the detection results of total organic carbon.43 total organic carbon standard solution samples concentrations of 25~150 mg·L^-1 were prepared in the experiment. 35 total organic carbon standard samples were collected in the first period, and the spectra were divided into training and test sets. The total organic carbon detection algorithm model was established and verified. In order to evaluate the robustness of the algorithm model, the spectra of the remaining 8 standard samples were collected in another period for test verification. The experimental results show that the total organic carbon quantitative model established by Ada-iPLSR has high accuracy and robustness. The root means square errors of group verification and test verification are 1.304 and 1.533 mg·L^-1 respectively, which are better than partial least squares regression and Extreme Learning Machine methods. In order to further verify the effectiveness of this method, this modeling method is used to predict the total organic carbon content of domestic sewage. The actual surface water samples were taken from the sewage at the sewage outlet of Gaocheng sewage treatment plant in Shijiazhuang, Hebei and the domestic sewage in the park of Hebei Xianhe company. After dilution, 50 surface water samples were obtained. SPXY method was used to divide them into 33 water samples in the training set and 17 water samples in the test set. In the actual water sample detection, the net signal analysis method is used for spectral pretreatment to reduce the interference of other substances in surface water on the detection of total organic carbon. The root means square error of group verification prediction is 3.26 mg·L^-1, and the average absolute value percentage error is 3.46%. To sum up, the Ada-iPLSR method can quickly and accurately detect the total organic carbon in surface water, providing a method support for the on-line detection of total organic carbon in water quality.