As a common machine learning method, BP neural network model is widely used in species distribution models to analyze the relationship between biological distribution and environmental factors. Compared with the traditional regression models, this model can flexibly deal with the nonlinear relationship between variables. However, there are substantial uncertainties in parameter setting as a result of its complex structure, which may affect the prediction and application of this model. This study considered approaches to optimize the model parameters, including the group method of data handling, genetic algorithm and adaptive algorithm, to improve initial weights and the number of hidden nodes of the model, respectively. Seven combinations of optimized BP models were implemented based on the survey data obtained from fishery resources and environment in Shandong offshore between 2016 and 2017. Our results showed that there were significant differences in the predictive performance of the seven optimization models. The predictive performance of the one-way and two-way optimization models was approximately the same. The root mean square error and the square of residual error were 0.35 and 1.94 respectively, which were smaller than the initial model's 0.52 and 2.40, and the maximum correlation coefficient was 0.45, indicating that the optimization effect of the model was the best. After the comparison and optimization, it was found that the resource density of Oratosquilla oratoria was basically different with the increase of bottom salinity while the resource density of O. oratoria was significantly different with the increase of bottom temperature. In addition, the increase of water depth in the optimal model compared with the initial model was a key environmental factor,which had an important effect on the resource density of O. oratoria. In this study, the parameter optimization method of the BP neural network model was further developed, which proved that the parameter optimization had important effect on the prediction performance of the BP model, and the model optimization was of great significance for the analysis of the relationship between resource density and environmental factors.