In the field of TSV 3D integration, due to the miniaturization of internal defects and the challenges associated with non-contact detection, identifying a non-destructive, sensitive, and efficient method for internal defect detection is crucial. In response to this challenge, a TSV internal defect detection method based on a temperature sensor array has been proposed. Internal defects influence the external temperature distribution of TSV 3D packaged chips, displaying regular patterns of change. Each type of defect causes different deviations in the external temperature distribution. By utilizing a temperature sensor array to measure these distribution changes, effective identification and classification of defects can be achieved. A detection system, based on the temperature sensor array, was designed to reveal the internal defect information based on the thermal signals generated by chips under operational conditions. Through theoretical analysis and simulation modeling, a model simulating the temperature distribution and thermal changes of chips under working conditions was developed. In the experiments, based on the preparation of chip samples and the setup of a testing platform, effective classification of internal defects was achieved using a classification recognition model, reaching an accuracy rate of up to 99.17%. This detection method provides a cost-effective and efficient new approach for the reliability analysis and fault diagnosis of high-density and miniaturized chips.