A pixel classification-based multiscale Unmanned Aerial Vehicle （UAV） aerial object rotational tracking algorithm was proposed for the UAV tracking process， in which the vertical tracking box limited the tracking accuracy when dealing with scale changes， similar objects and aspect ratio changes. Firstly， MS-ResNet （MultiScale ResNet-50） was designed to extract multiscale features of the object. Then， a pixel binary classification module was designed on the multi-channel response map with orthogonal characteristics to further refine the results of classification and regression branches accurately. Meanwhile， to improve the pixel classification accuracy， the concurrent spatial and channel “Squeeze & Excitation” （scSE） module was used to filter the object features in the spatial and channel domains. Finally， a rotational tracking box fitting the actual size of the object was generated based on pixel classification to avoid the contamination of positive samples. Experimental results show that the proposed algorithm has the success rate and precision on the UAV tracking dataset UAV123 of 60.7% and 79.5% respectively， which are 5 percentage points and 2.7 percentage points higher than those of Siamese Region Proposal Network （SiamRPN） respectively， and has the speed reached 67.5 FPS， meeting the real-time requirements. The proposed algorithm has good scale adaptation， discrimination ability and robustness， and can effectively cope with UAV tracking tasks.