Land cover classification based on multi-temporal remote sensing data is more accurate than that base on single temporal data. Owing to the low frequency of repeated observations by medium and high resolution sensors, the classification accuracy based on time series images is severely restricted. Spatio-temporal fusion technology is an effective method to address the lack of time series observation data, but its application in classification research based on time series data has not been fully verified. In order to solve this problem, in this article a part of Liaoning Province was taken as the research area, Landsat and MODIS data as research objects, STARFM, ESTARFM, and Semi-Physical fusion models as annual Landsat time series data generation methods, and Random Forest, Maximum Likelihood, and Support Vector Machine methods as time-series classifiers, the collaborative classification accuracy of different fusion models and classifiers. The experimental results show that the spatio-temporal fusion processing can effectively improve the accuracy of time-series classification, especially vegetation-type features,it is not sensitive to the choice of classifier.