Many studies apply Transformer to time series prediction tasks. However, compared with other time series, motion trajectory data has kinematic uncertainty without obvious periodicity. To reduce noise interference and enhance trend modeling, this study proposes a target trajectory prediction method based on time-frequency domain information fusion and multi-scale adversarial training based on Transformer architecture. The wavelet decomposition is embedded into the network model to realize the adaptive filtering in the time-frequency domain, and then time-domain attention is integrated to encode the long-term trend characteristics of the observed trajectory more effectively. Meanwhile, the study designs a full convolution discriminator to further improve the prediction accuracy by learning multi-scale short-term micro motion representation of the sequence through adversarial training. A trajectory prediction dataset DT including 2D ship trajectory and 3D aircraft trajectory is established as a benchmark, and comparative experiments with Transformer, LogTrans, Informer, and other models are conducted. Experiment results show that the proposed method is superior to other models in the tasks of medium and long-term trajectory prediction.