A method for abnormal account prevention and control based on a graph structure model, comprising: obtaining a graph structure model trained by using labeled samples, wherein the graph structure model is trained based on an account relationship network, and the account relationship network comprises a plurality of nodes, and edges corresponding to relationships between the nodes, wherein the plurality of nodes include an account, device information, network information, location information, and identity information, wherein each labeled sample comprises a label indicating whether a corresponding node is an abnormal node. wherein training the graph structure model based on the account relationship network using labeled samples comprises: for each node included in the plurality of nodes of the account relationship network, the graph structure model is used to calculate an embedding vector of the node after multiple iterations in a hidden feature space corresponding to the node based on an original feature of the node and a feature of an edge of the node that is associated with another node and an embedding vector of the node in a previous iteration for the node in the hidden feature space corresponding to the node, wherein the trained graph structure model maximizes a consistency between (i) a predicted result of a node based on the embedding vector of the node and (ii) the label of the labeled sample corresponding to the node; calculating, by using the trained graph structure model, an embedding vector that corresponds to a to-be-tested sample, and a prediction probability that corresponds to the to-be-tested sample based on the embedding vector that corresponds to the to-be-tested sample; and performing abnormal account prevention and control on the to-be-tested sample based on the embedding vector that corresponds to the to-be-tested sample, and based on the prediction probability that corresponds to the to-be-tested sample, wherein the to-be-tested sample is the account and if the calculated prediction probability that corresponds to the account is greater than a specified threshold, the account is determined as an abnormal account, and then the abnormal account is deregistered. obtaining a graph structure model trained by using labeled samples, wherein the graph structure model is trained based on an account relationship network, and the account relationship network comprises a plurality of nodes, and edges corresponding to relationships between the nodes, wherein the plurality of nodes include an account, device information, network information, location information, and identity information, wherein each labeled sample comprises a label indicating whether a corresponding node is an abnormal node. wherein training the graph structure model based on the account relationship network using labeled samples comprises: for each node included in the plurality of nodes of the account relationship network, the graph structure model is used to calculate an embedding vector of the node after multiple iterations in a hidden feature for each node included in the plurality of nodes of the account relationship network, the graph structure model is used to calculate an embedding vector of the node after multiple iterations in a hidden feature space corresponding to the node based on an original feature of the node and a feature of an edge of the node that is associated with another node and an embedding vector of the node in a previous iteration for the node in the hidden feature space corresponding to the node, wherein the trained graph structure model maximizes a consistency between (i) a predicted result of a node based on the embedding vector of the node and (ii) the label of the labeled sample corresponding to the node; (i) a predicted result of a node based on the embedding vector of the node and (ii) the label of the labeled sample corresponding to the node; calculating, by using the trained graph structure model, an embedding vector that corresponds to a to-be-tested sample, and a prediction probability that corresponds to the to-be-tested sample based on the embedding vector that corresponds to the to-be-tested sample; and performing abnormal account prevention and control on the to-be-tested sample based on the embedding vector that corresponds to the to-be-tested sample, and based on the prediction probability that corresponds to the to-be-tested sample, wherein the to-be-tested sample is the account and if the calculated prediction probability that corresponds to the account is greater than a specified threshold, the account is determined as an abnormal account, and then the abnormal account is deregistered.