  Disruption prediction has made rapid progress in recent years, especially in
machine learning (ML)-based methods. Understanding why a predictor makes a
certain prediction can be as crucial as the prediction's accuracy for future
tokamak disruption predictors. The purpose of most disruption predictors is
accuracy or cross-machine capability. However, if a disruption prediction model
can be interpreted, it can tell why certain samples are classified as
disruption precursors. This allows us to tell the types of incoming disruption
and gives us insight into the mechanism of disruption. This paper designs a
disruption predictor called Interpretable Disruption Predictor based On
Physics-guided feature extraction (IDP-PGFE) on J-TEXT. The prediction
performance of the model is effectively improved by extracting physics-guided
features. A high-performance model is required to ensure the validity of the
interpretation results. The interpretability study of IDP-PGFE provides an
understanding of J-TEXT disruption and is generally consistent with existing
comprehension of disruption. IDP-PGFE has been applied to the disruption due to
continuously increasing density towards density limit experiments on J-TEXT.
The time evolution of the PGFE features contribution demonstrates that the
application of ECRH triggers radiation-caused disruption, which lowers the
density at disruption. While the application of RMP indeed raises the density
limit in J-TEXT. The interpretability study guides intuition on the physical
mechanisms of density limit disruption that RMPs affect not only the MHD
instabilities but also the radiation profile, which delays density limit
disruption.
