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Mikania micrantha has strong climbing ability and amazing growth speed, which poses a serious threat to the surrounding ecological environment and biodiversity. Satellite remote sensing data is the main data source for identification and prediction of Mikania micrantha. However, the existing data have limitations such as low resolution, long transit time and cloud shielding, and the accuracy of identification and prediction of Mikania micrantha is low. In view of this, a method for automatic identification of Mikania micrantha outbreak area and invasion probability prediction based on airborne laser data and aerial multispectral data was proposed.Object-oriented multi-scale segmentation method was used to automatically identify the outbreak points of Mikania micrantha in the study area, and Logistic regression method was used to predict the invasion distribution probability of Mikania micrantha by using canopy height model, vegetation coverage, slope and slope aspect data in the forest farm. The results showed that the object-oriented multi-scale segmentation method could extract the Mikania micrantha outbreak area in the study area, and the identification accuracy was high, the misclassification rate was 4.66%, and the missed detection rate was 0.41%.Logistic regression model had a good prediction effect on the invasion distribution probability of Mikania micrantha, and the correct rate was 88.46%.This method can realize accurate identification and prediction of Mikania micrantha in a wide range, and can serve for comprehensive prevention and control and monitoring of Mikania micrantha, providing strong support for invasion monitoring of Mikania micrantha.