The defect classification task is of great benefit to evaluating the safety performance of equipment and providing useful feedback information for discovering production process problems. In this article, we present a semisupervised learning (SSL) framework for transient thermography detection to employ the temporal and spatial information encoded into the three-dimensional transient thermal tensor data and provide pixel-level classification results for defect types. The time- and frequency-domain physical models for the transient thermal evolution of different kinds of defects are established to illustrate the theoretical foundation of defects classification based on transient thermography. The semisupervised multiclass Laplacian support vector machine is proposed to enable involving the abundant unlabeled data for enhancing learning performance in practical industrial applications where labeled samples are insufficient and labeling work is costly and laborious. A case study on silicone insulating materials with various types of artificial simulated internal defects validates the stronger generalized ability of the proposed method. This work, for the first time, proposes an SSL framework in transient thermography-based defect detection studies. It is believed that our proposed method is quite inspired for introducing SSL techniques to transient thermography for preferable performance in practical industrial applications.