Object tracking algorithms based on Siamese networks usually adopt simple cross-correlation matching， but this simple matching method will introduce a lot of irrelevant information and weaken the response of the target region. Although the Siamese tracking network without anchor frame avoids the adjustment of anchor frame parameters， it cannot adapt well to the scale change of the target due to the loss of priori information. Therefore， aiming at the above problems， this paper proposes a object tracking matching enhancement algorithm SiamBM based on Siamese networks. By encoding the boundary frame coordinate information of the target， effective guidance information is provided for the tracking model. The discriminant ability of the tracking model is further improved by means of depth separable cross-correlation and cascade pixel matching cross-correlation. Multi-scale cross-correlation is adopted to enhance the scale adaptability of the tracking model. In the OTB100 dataset， the success rate and accuracy rate of SiamBM reached 0.684 and 0.906， respectively， which increased by 5.2% and 4.2% compared with the benchmark model. The experimental results show that compared with the current mainstream trackers， SiamBM has achieved quite competitive results and superior performance in various dataset indicators.