With the development of deep learning technology, attackers can obtain potentially sensitive information from public geological data through classification, prediction, and other methods, which could lead to the leakage of important geological data. To solve the above problems, we propose a geological drilling data protection model based on graph adversarial attack Gcntack.Based on the degree properties of geological data topology, we first generate attacks that satisfy the same power-law distribution as tiny node disturbance. It can ensure that the adversarial attacks are not easy to be found, and while can change the classification result of the target node. Secondly, we introduce an attention mechanism. Using a graph attention network model based on interpretability, we analyze the properties of key nodes that directly affect the results of the adversarial attacks, so as to verify the rationality of the selecting adversarial nodes in the Gcntack model. Finally, a comprehensive evaluation, based on the benchmark dataset and geological drilling dataset, is presented to show this proposed scheme can reduce the prediction accuracy of attackers and achieve the purpose of protecting important geological drilling data.