The invention provides an intelligent
toll evasion behavior classification method, a storage medium and a terminal, and belongs to the field of highway billing and charging. The method comprises the steps of collecting historical
toll evasion cases and normal passing cases, establishing a
toll evasion occurrence and type judgment rule model, and verifying and applying the model. The final fee evasion occurrence and type judgment rule model is associated with a highway
station charging
system, real-time passing vehicles are monitored, whether fee evasion behaviors occur or not is judged, the fee evasion type is judged, and finally a monitoring result is visually displayed to workers. In the method, a
machine learning technology is introduced,
machine learning model training is carried out by utilizing extracted features through
random forest, LASSO, a
decision tree,
logistic regression, GBDT and other algorithms in a model, and a corresponding
machine learning model for detecting whether toll evasion occurs or not and judging the type is generated; besides, new abnormal features are screened in
model application, a new type is manually judged and added, and the learning ability and the application range of the model are improved.