There are internal relationships between the spatial and temporal distribution of species and environmental factors; however, the randomness and uncertainty of marine ecosystems prevent demonstrating the relationships in an easy way. Based on the seasonal bottom trawl survey data collected from 2011 to 2016 in Haizhou Bay and its adjacent waters, we used three species distribution models, including GLM, GAM and Random Forest to study the spatial distribution of Portunid crab (Charybdis bimaculata) and analyze the relation with environmental factors. We selected influential environmental variables and built models according to criteria such as AIC, deviance explanation and cross validation. The effects of environmental variables on the distribution of C. bimaculata were evaluated on the basis of the SDMs. The results showed similar results among the three models in the interpretation of the relationship between environmental variables and crab distribution. The GAM provided better fit to data, whereas random forest have superior predictive performances than the other models. The models illustrated significant variations of crab abundance among years and seasons, which contributed to over 18 percent and 3.8 percent of total deviance, respectively. Depth and sea surface salinity were influential environmental factors for relative abundance, both positive correlated with relative catches. The spatial distribution of Portunid crab was featured by high catches in northeast area and low in southwest, in accordance with the water depth in Haizhou Bay. We predicted the spatial distribution of C. bimaculata across seasons using FVCOM simulation data and the random forest model to facilitate the exploitation and conservation of the fishery resources.