Hydrophobicity Class (HC) is one of important indexes to measure the performance of composite insulators. The hydrophobicity of insulator shed is different on the part surface for the various factors in the natural environment. In order to judge the performance of insulator, this paper proposes a region adaptation method for insulator segmentation and hydrophobicity recognition based on deep learning. First, separate the insulator area and the background by the insulator segment module, which provides segment information for the later operators on the insulator area; then, the insulator area is cropped into several image blocks with the fixed resolution, which can reduce the resolution and the operational complexity while preserving the insulator surface details; finally, judge the hydrophobicity class of insulator by the hydrophobicity classification module. The experiment dataset from maintenance sites is used to build model in stages and evaluate separately the accuracy of the stage of segment and HC classification. The experiment results show that the segment stage module can identify the insulator regions and the background, whose accuracy on the cross-validation test dataset is greater than 97.21%, and the HC classification stage module can classify the HC of insulators, whose accuracy of 140 test images can reach 98.65%. The proposed model is proven to be an effective solution to checking insulators performance in complex natural environment by experiments.