Air-conditioning condenser (ACC) is a key component of air-conditioning equipment. ACC tube leakage damage will cause huge economic losses and energy waste. The laser ultrasonic guided-wave detection method is suitable for ACC damage detection due to its high sensitivity and noncontact advantages. However, the ACC tube size is small and the weld joint is not standardized, which makes it difficult for the laser to aim at the tube. And the detection signals generated by laser excitation at different laser incident angles are different. In this article, the unsupervised multisource adaptation adversarial network (UMAAN) is proposed to improve the generalization ability of the ACC detection model. First, the feature diagrams of the signal are obtained by time–frequency analysis, and the diagrams are set to different source and target domains according to different incident angles of laser excitation. Then, the shared feature extractor is used to obtain features of all samples. And the model parameters are optimized by adversarial network to make the features distance of the target and source domains closer. Among them, the adaptive weight coefficients are designed to adjust the loss value weights of different source domains. And the label error weight factor is designed to improve the damaged signals detection accuracy by adjusting the loss weight of damaged samples and normal samples. Finally, the damage detection experiments of the ACC are carried out. The experimental results illustrate that the proposed UMAAN can achieve the high-precision detection of ACC damages.