Abstract: BP neural network model, which could be thought of as being related to artificial intelligence, machine learning, parallel processing and statistics, is being used more and more common. In the present study, a three layer BP neural network model of bisphenol A (BPA) adsorption onto the surfacial sediments (SSs) sampled from Songhua River in Jilin Province had been established to simulate the influence of several different factors, such as solution to solid ratio, contents of non-residual fractions (organic matters, Fe oxides and Mn oxides), and initial concentration of BPA, on the adsorption capacity of BPA. The correlation coefficient (R²) of the established BP neural network model was 0.966 5, which was larger than 0.8. The mean square error of the calibration set (MSE_c), the root mean square error of validation set (MSE_v), and the mean square error of the predication set (MSE_p) was 0.006 8, 0.059 6, and 0.128 5, respectively. The maximum adsorption of BPA adsorbed onto SSs collected from Songhua River was estimated and calculated by genetic algorithms (GA). GA is a search technique used in computing to find exact or approximate solutions to optimization and search problems and it is categorized as global search heuristics, on the basis of the established BP neural network model. The optimized results of the maximum capacity of BPA absorbed onto SSs (without treatment, H₂O₂ extraction, NH₂OH·HCl extraction, and (NH₄)₂C₂O₄ extraction) were 0.532 mg/g, 0.502 mg/g, 0.917 mg/kg and 0.8992 mg/g, and under the same conditions, the experimental values were 0.542 mg/g, 0.445 mg/g, 1.081 mg/g and 0.836 mg/g, respectively. The relative errors between the optimal values by GA and the experimental ones were in the range of 0.96%~8.21%. The amount of BPA adsorbed onto SSs was predicted using the established BP neural network model as a function of non-residual fractions including organic matters, Fe oxides and Mn oxides on a mass or a molar base, respectively. The predicted results of the maximum BPA adsorption capacity on a mass base showed that there has been a general uptrend of the BPA adsorption with the increase of Fe oxides and organic matters and a general downtrend with increase in Mn oxides. Meanwhile, the maximum capacity of BPA adsorbed onto SSs indicated the same results on a molar base with the results obtained on a mass base. The relative contributions of the BPA maximum adsorption onto SSs, expressed by the ratio of the mass of adsorbed BPA to the contents of non-residual fractions, were calculated as follows: K_Fe=0.002 8, K_OMs=0.000 2 and K_Mn=-0.031 8, respectively. It could be inferred that both of the Fe oxides and organic matters have positive effect on the BPA adsorbed by SSs, while Mn oxides inhibited the adsorption of BPA onto SSs. Hence, the contributions of the non-residual fractions (including organic matters, Fe oxides and Mn oxides) onto SSs to the maximum adsorption of BPA followed the order as: K_Fe>K_OMs>K_Mn. The fact that Fe oxides was confirmed as the main binding site for BPA adsorption onto SSs was demonstrated through the mechanism analysis through the established BP neural network model, yet the reasons why adverse effect of Mn oxides on the adsorption of BPA onto SSs should be further studied.