Spatial interpolation is a method used to predict data in unsampled areas based on sampled points. Traditional methods have limitations in capturing complex spatial patterns. To address this, a new model called GSARNN was developed, combining neural networks to improve spatial interpolation in three-dimensional space. The GSARNN outperformed other methods in accuracy and adaptability, showing its effectiveness in predicting spatial data.