In view of the heavy task, low efficiency, and high cost of existing airport bird-repelling systems, we designed a laser bird-repelling robot system composed of a camera, dual-axis mirror, laser, video processing, and servo controls. The flying bird stabilized sighting algorithms in the system consists of target detection, target tracking, and servo control algorithm. To solve these drawbacks, we applied a simple and effective nearest neighbor classifier in the tracking process to confirm the tracking target for improving tracking stability and mitigating the lack of target loss judgment in the classical kernelized correlation filter (KCF) tracking algorithm. Furthermore, we replaced the concatenated histogram of oriented gradient (HOG) features in the original KCF algorithm with a feature map of approximate size in the middle layer of the depth network to solve the weak discrimination of the HOG feature in the KCF method. This strategy enhances the discrimination of target features while avoiding extracting depth features block by block. Experimental results show that the proposed laser bird-repelling robot system accurately stimulates and interferes with flying birds, and its detection performance and tracking performance outperform other algorithms. The proposed system serves as an effective and safe stimulus signal bird-repelling scheme in airports.