In order to solve the problem of inaccurate measurement results due to environmental factors while measuring the sediment concentration of rivers with the capacitance method, a fusion model based on Kalman filtering and LSTM (Kalman-LSTM) is proposed. Firstly, the Kalman filtering is used for filtering to reduce the random error of sensor measurement. Then, the LSTM was used to integrate multi-sensor data of sediment content information and environmental content information, to reduce the influence of environmental factors on sediment content measurement by the capacitance method. Finally, a Kalman-LSTM fusion model for measuring sediment concentration by capacitance method was developed. To verify the fusion effect of the Kalman-LSTM fusion model, the root mean square error, maximum absolute error, average absolute error, and average relative error of each model are compared with the BP model, the RBF model, and the LSTM model. The experimental results show that the average relative error of the Kalman-LSTM fusion model is 2. 54% and the root mean square error is 2. 47 kg / m 3 . The fusion model can effectively reduce the influence of environmental factors on sediment concentration measurement and improve the accuracy of sediment concentration measurement by the capacitance method.