The invention provides a GRU based
recurrent neural network multi-
label learning method, which comprises the steps of S1, initializing a
system parameter [theta]=(W, U, B); S2, inputting a sample {xi,yi}<i=1><N>, calculating a hidden state hT of the output of an RNN (
Recurrent Neural Network) at each moment, wherein the sample xi belongs to R<M*1>, yi is a multi-
label vector of the sample xi, andyi belongs to R<C*1>; S3, calculating a
context vector hT and output zi of an output layer; S4, calculating the predicted output yi^, calculating the loss Li, and determining an objective function J;S5, solving the gradient of the
system parameter [theta]=(W, U, B) according to a
gradient descent method and a BPTT (Back-propagation Through Time)
algorithm; S6, determining a learning rate [eta],and updating each weight gradient W=W-[eta]*[
delta]W; S7, judging whether the neural network reaches stable or not, if so, executing a step S8, if not, returning the step S2, and iteratively updatingmodel parameters; and S8, outputting an optimization model. According to the invention, effective feature representation of the sample can be obtained by fully utilizing the RNN so as to improve the accuracy of multi-
label classification. In addition, a problem of gradient disappearance is not easy to occur in back propagation.