Soil erosion monitoring in coastal mountainous areas is very important during the construction of Electric-Transmission-Line (ETL) because of the impact this disturbance has on the sensitive environment. In this study, high-resolution remote sensing data and deep learning models including Dense and Long Short-Term Memory (LSTM) were used to fit the popular soil erosion equation, which is called the Revised Universal Soil Loss Equation (RUSLE), for the Min-Yue ETL (in Fujian). The accuracy of soil erosion regression was then evaluated in the transmission line buffer area and sampling spots at two spatial scales in order to obtain the optimized parameters and a suitable model. The results show that the Dense and LSTM models can meet the accuracy requirements by using 10 characteristic values, including soil erodibility, annual rainfall, mountain vegetation index (NDMVI), DEM, slope, four bands gray values of high-spectral image, construction attributes. The optimized parameters for the priority machine-learning model LSTM are as follows: the layer depth is 3, the layer capacity is 512, the dropout ratio is 0.1, and the epoch of the LSTM model is 7060. The regression accuracy of the LSTM model decreases with an increase in soil erosion levels, and the average regression accuracy is greater than 0.98 for the slight level of soil erosion. Therefore, the machine-learning model of LSTM can be applied for quickly monitoring the soil erosion using high resolution remote sensing data.