The invention relates to a dual-
mechanism analysis method and device for an
aggregation rate prediction model of nanoparticles. The method comprises the following steps: collecting experimental data related to the
aggregation rate of the nanoparticles as an input
feature set; wherein each feature in the input
feature set is marked with an
aggregation rate label; inputting the input
feature set into a
machine learning model for training and testing to obtain a
nanoparticle aggregation rate prediction model; calculating an SHAP absolute value of each feature in the input feature set by adopting an SHAP method, and screening out at least two features from the input feature set according to the SHAP absolute values to serve as a key drive feature set; and two features are selected from the key driving feature set, and visual
verification of interaction of the two selected features is carried out. Therefore, the influence of a single feature is considered, the interaction mechanism between the features and the influence of the interaction mechanism can be revealed, and then the
black box model can be converted into a transparent and credible
scientific analysis tool.