The target tracking algorithm based on deep learning takes the deep convolution output as the feature, which is high in accuracy but time-consuming. The target tracking algorithm based on fusion features fuses the target features according to the response value, although the tracking speed is fast, but the accuracy is reduced. In order to consider the timeliness and accuracy of the target tracking algorithm, a target tracking algorithm based on similarity feature estimation is proposed. First, sampling importance resampling filter particle is used to construct the target observation model, which includes selection of particle state, transfer system state, construction of observation model, particle weight update, and resampling process. On this basis, the statistical texture features, moving size features, moving speed, and direction features of the target are extracted, and the target feature framework is constructed by using the target features. The target positioning is estimated based on the similarity features, including describing the target model, representing the candidate model, measuring the specific similarity of the target, and the target positioning process. After the target positioning, the target tracking is realized based on real-time compression. The tracking accuracy of the proposed algorithm is above 90%, the tracking time is kept below 450ns, and the performance of this algorithm is better than that of the target tracking algorithm based on deep learning and fusion features. The proposed algorithm can track the target quickly and accurately, and has strong application advantages.