A refractive index structural constant C2n and three kinds of conventional meteorology parameters (temperature, wind speed and relative humidity) are chosen from ship-based measurement in Sanya from 2016-01-06 to 2016-01-09. Two models are established based on the backward propagation neural network and the stepwise regression theory, and three-day estimation of C2n is carried out. The results show that the variation tendency and magnitude of the results estimated by two models are in accord with general characteristics and change rule of the optical turbulence near the sea, and these results demonstrate the fundamental diurnal variation of C2n. Overall correlation coefficients are 0.8661 and 0.8496. Statistical variables of mean absolute error, mean relative error, root mean square variance and relative coefficient are used to evaluate the estimation results. Further analysis shows that both the two models can calculate C2n near the sea precisely. However, when weak turbulence occurs at night, the estimation results are slightly higher than measurement results. To further improve the estimation accuracy, the estimation effect during nighttime should be improved.