A three-dimensional neural network model has been designed for representing the phase equilibrium data related to aqueous two-phase systems. The polyvinyl pyrrolidone/phosphate/water system was selected as the model system to demonstrate the point of interest. The collected experimental data were categorized into two subsets, training and validation sets, not only to find the suitable network configuration but also to prevent the overfitting problem. Meanwhile, the weight comparison method was proposed to optimize the three-dimensional neural net. The results of accuracy comparison indicate that it outperforms the two-dimensional neural network on some details and can further enhance the calculation accuracy of the phase equilibrium data for these investigated aqueous two-phase systems. The development of the neural network in the three-dimensional space should be a research project of concern.