The invention belongs to the technical field of meteorological drought prediction, and particularly relates to a meteorological drought prediction method and device based on a VMD-CNN-BiLSTM-ATT hybrid model, and the method comprises the steps: obtaining historical meteorological data, taking the historical meteorological data as input data, carrying out the variational mode decomposition of the input data, obtaining a plurality of intrinsic mode components IMF, respectively splitting each IMF component into a training set and a test set; inputting data of the training set into an input layer of the convolutional neural network, and calculating to obtain an output matrix; taking a matrix obtained by pooling as the input of a bidirectional long-short-term memory network, processing data from the forward direction and the reverse direction at the same time, and paying attention to the correlation between the future moment and the current moment; adding an attention mechanism layer to the output side of the bidirectional long-short-term memory network, adding weights to the hidden layer feature vectors, and calculating output data, namely predicted values, again; subjecting all CNN-BiLSTM-ATT predicted values to recombination and superposition, and obtaining an output sequence. Compared with a traditional drought prediction method, the method is smaller in prediction error and higher in prediction precision and credibility.