Based on the open datasets of ASAS-SN, Gaia and ZTF, we construct a compatible light curve dataset named LEAVES for automated recognition of variable stars, which can be used for training and testing new classification algorithms. The dataset contains a total of 977,953 variable and 134,592 non-variable light curves, in which the supported variables are divided into 6 superclasses and 9 subclasses. We validate the compatibility of the data set through experiments and employ it to train a hierarchical random forest classifier. Experimental results prove that the classifier is more compatible than the classifier established based on a single band and a single survey, and has wider applicability while ensuring classification accuracy. Here we provide the light curves of these objects by class in csv files.