With the continuous increase in through-silicon via (TSV) packaging density, the detection of the defects hidden inside the package has become increasingly challenging. To address the difficulties in detecting multiple defects inside TSVs and the low efficiency of such detection, a method based on active infrared excitation for TSV internal defect classification and localization is proposed. First, through simulation analysis, the external performance patterns of TSV internal defects under active excitation are studied. Then, a convolutional neural network classification model is constructed and trained using simulated data to achieve the classification and localization of multiple internal defects. Finally, a platform for detecting internal defects in packaging is established to conduct experimental research. Near-infrared laser is used as the active excitation to stimulate internal defects in the packaging model. Infrared thermography is employed to capture images, which are then fed into the convolutional neural network for classification. The results show that this method can effectively identify the defect types and locations without damaging the samples, with an accuracy rate reaching 96.20%. It provides a reliable approach for the reliability analysis of TSV 3D packaging.