Abstract: Post insulator is an important component in substation, which is prone to failure in complex working environment. However, traditional manual detection is difficult to identify a large number of post insulator infrared images quickly. For this reason, this paper proposes an artificial intelligence (AI) recognition method of post insulator infrared image based on improved cascade Gentel Adaboost classifier. A large number of infrared images collected in the field are used to construct the post insulator infrared data set, then the Haar-like eigenvalues of the post insulator data set are calculated, and different eigenvalues are constructed into several weak classifiers. By improving the Gentel Adaboost algorithm, the weak classifier training is integrated into the strong classifier, and the cascade Gentel Adaboost classifier is obtained to realize the multi-target accurate recognition of post insulator in the infrared image. The results show that the proposed method can be adopted to recognize the post insulator in different backgrounds with 94.2% accuracy, in the meantime, the infrared temperature characteristics of post insulator can be retained while correctly identifying and locating, which provides an effective way for intelligent identification and fault diagnosis of post insulator.