Objective This paper constructs a generalized regression neural network （GRNN） model to predict the disintegration time of traditional Chinese medicine （TCM） tablets.Method Taking Astragali Radix as a model drug， the mixed Astragali Radix powders with different powder properties were prepared by mixing Astragali Radix extract powders with microcrystalline cellulose and lactose， which were made to Astragali Radix tablets by direct compression method. The powder properties of mixed Astragali Radix powders and the disintegration time of Astragali Radix tablets were determined， respectively. The correlation between the original data was eliminated by principal component analysis （PCA）. The principal component factors were used as the input layer of the GRNN model， and the disintegration time was used as the output layer for network training. Finally， the verification group data was used to predict the disintegration time， and the network prediction accuracy was calculated by comparing with the actual value.Result Three principal component factors were obtained through PCA by analyzing the original nine variables that were correlated with each other （Hausner ratio， true density， tap density， compression degree， angle of repose， bulk density， porosity， water content and total dissolved solids）， which reduced the complexity of the network. The prediction value of the disintegration time based on this prediction method was in good agreement with the actual value， the error of disintegration time was 0.01-1.34 min and the average relative error was 3.16%.Conclusion Based on the GRNN mathematical model， the physical properties of Astragali Radix extract powders can be used to accurately predict the disintegration time of Astragali Radix tablets， which provides a reference for studying the disintegration time of TCM tablets.