A method for human pose prediction, implemented by an electronic device, the method comprising: invoking a basic neural network to perform prediction on an inputted target image, to obtain an initial prediction graph of a human key-point; inputting the initial prediction graph of the human key-point and a human structure graph into a pose graph neural network, each node in the human structure graph corresponding to a human joint respectively, and each edge connecting adjacent human joints; invoking the pose graph neural network to initialize the human structure graph by using the initial prediction graph of the human key-point, to obtain an initialized human structure graph; and invoking the pose graph neural network to perform iterative prediction on the initialized human structure graph, to obtain a final prediction graph, the final prediction graph comprising a predicted human pose, wherein the pose graph neural network comprises a convolution kernel and a gated recurrent unit; and the invoking the pose graph neural network to perform iterative prediction on the initialized human structure graph, to obtain the final prediction graph comprises: collecting information about an adjacent node of each node in the human structure graph by using the convolution kernel before each iterative update, to obtain adjacent node information; updating a state of the node according to the adjacent node information by using the gated recurrent unit, the gated recurrent unit being used for digging spatial information between adjacent nodes in the human structure graph; and obtaining the final prediction graph after a predetermined number T of times of update. invoking a basic neural network to perform prediction on an inputted target image, to obtain an initial prediction graph of a human key-point; inputting the initial prediction graph of the human key-point and a human structure graph into a pose graph neural network, each node in the human structure graph corresponding to a human joint respectively, and each edge connecting adjacent human joints; invoking the pose graph neural network to initialize the human structure graph by using the initial prediction graph of the human key-point, to obtain an initialized human structure graph; and invoking the pose graph neural network to perform iterative prediction on the initialized human structure graph, to obtain a final prediction graph, the final prediction graph comprising a predicted human pose, wherein the pose graph neural network comprises a convolution kernel and a gated recurrent unit; and the invoking the pose graph neural network to perform iterative prediction on the initialized human structure graph, to obtain the final prediction graph comprises: collecting information about an adjacent node of each node in the human structure graph by using the convolution kernel before each iterative update, to obtain adjacent node information; updating a state of the node according to the adjacent node information by using the gated recurrent unit, the gated recurrent unit being used for digging spatial information between adjacent nodes in the human structure graph; and obtaining the final prediction graph after a predetermined number T of times of update. collecting information about an adjacent node of each node in the human structure graph by using the convolution kernel before each iterative update, to obtain adjacent node information; updating a state of the node according to the adjacent node information by using the gated recurrent unit, the gated recurrent unit being used for digging spatial information between adjacent nodes in the human structure graph; and obtaining the final prediction graph after a predetermined number T of times of update.