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In order to reduce the risk of accidents and predict and respond to abnormal situations in the complex and ever-changing flight environment faced by unmanned aerial vehicles (UAVs) during mission execution, a spatiotemporal collaborative trajectory prediction method based on Transformer model for quadcopter UAVs is studied. Collect the original trajectory of a quadcopter drone, implement outlier removal and missing point interpolation processing to optimize and clean up the original trajectory data for subsequent trajectory prediction. By combining deep learning and representation learning methods, data dimensionality reduction is achieved, and precise prediction of unmanned aerial vehicle spatiotemporal cooperative trajectories is achieved based on the Transformer model. The experimental test results show that although the prediction results of the design method have a slight deviation from the actual coordinate points, the overall results are within an acceptable range. The mean square error of all data in the validation set is only 0.32m when the number of data is 300, and the R-square test result is closest to 1, indicating good trajectory prediction ability.