The application discloses a
coal soluble
organic matter molecular category content prediction method based on a
solvent domain, relates to the field of
coal chemical analysis, and comprises the following steps: S1, acquiring sample data and recording
solvent domain identification; S2, performing component identification on each sample to obtain relative content; S3, merging component relative content into a molecular category content candidate set; S4, constructing a
feature matrix with component relative content as input and performing data preprocessing; S5, performing unsupervised evaluation on the molecular category content candidate set; and S6, training a prediction model and outputting a content prediction result. The sample is grouped through sequential
solvent sub-domain
processing, component features are unified, and molecular categories are merged, unsupervised target category screening is performed in combination with
principal component analysis and hierarchical
cluster analysis, accurate prediction of the
coal soluble
organic matter molecular category content is realized, and data support is provided for clean and efficient utilization of coal and optimization of a conversion process.