A candidate region twin-network tracking algorithm with a parallel hybrid attention mechanism is proposed to solve the problem of infrared target tracking under complex background interference. The parallel hybrid attention mechanism calculates spatial- and channel-attention feature maps in parallel. Subsequently, the spatial- and channel-attention feature maps are transformed into the same dimensions as the input feature map through dimensional expansion. Then, the extended spatial- and channel-attention feature maps are multiplied element-wise to effectively aggregate multiple attention weights, thereby obtaining a hybrid attention feature map. To dynamically adjust the weights of the corresponding elements in the original feature map based on the hybrid attention weights, the hybrid attention feature map and the input feature map are multiplied element-wise. A parallel hybrid attention mechanism is integrated into SiamRPN, and infrared aircraft targets under a ground/air background are tracked. Experimental results show that compared with SiamRPN, SiamBAN, and Mixformer, the success rate and accuracy of the proposed algorithm are improved by 15.5%, 1.8%, 8.5% and 20.1%, 9.3%, and 7.7%, respectively, whereas its tracking speed reaches 201 frames/s. The proposed method effectively realizes infrared target tracking under complex background interference and exhibits favorable real-time performance.