Sargassum thunbergii is the main component of intertidal algal beds. However, which statistical model best describes the Sargassum thunbergii quantitative distribution is poorly understood. In this study, 15 plots with an area of 25 m² were selected on Neizhe Island, Rongcheng, Shandong Province, China, for vegetation surveys and quantitative statistical analysis. A comparison of the arithmetic average, inverse distance weighted, and ordinary kriging models enabled their accuracy to be ascertained. The factors that might affect model accuracy were also tested, including population density, clumping index, and vegetation fractional coverage. The results showed that the performance of the inverse distance weighted model was the most stable and the average errors were the lowest (mean absolute error 39.1 individual, root mean square error 53.3 individual, rate of deviation 13.0%), while the accuracy of the arithmetic average model varied considerably and the average errors were the highest (mean absolute error 53.8 individual, root mean square error 65.3 individual, rate of deviation 14.6%). It was verified that population density and vegetation fractional coverage had no obvious influence on model precision (P > 0.05), but the clumping index clearly affected the mean absolute error and the root mean square error in the three models (P < 0.05). The results showed that there was no significant difference in the accuracy of the three models, and that the clumping index could influence model accuracy for some indicators. Generally, both the inverse distance weighted model and the ordinary kriging model had certain advantages due to a more stable performance, the lower mean error, and their ability to reveal the spatial distribution of the Sargassum thunbergii population.