Visual　object　tracking　methods　based　on　Siamese　network　are　often　difficult　to　distinguish　objects　with　the　same　semantic　or　similar　appearance　as　tracking　target　in　tracking　process　due　to　the　lack　of　discriminating　strategies　for　the　confusing　objects.　We　propose　a　visual　object　tracking　method　based　on　Siamese　modulation　network.　It　takes　the　given　bounding　box　in　the　target　frame　and　the　current　frame　as　input,　and　fuses　these　multi-layer　convolutional　features　to　obtain　more　target　appearance　information　of　bounding　box　and　the　current　frame.　The　feature　modulator　generates　feature　modulation　vector　based　on　the　given　bounding　box　to　enhance　visual　appearance　information　of　target　instance　in　multi-layer　feature　of　the　current　frame,　so　as　to　make　target　instance　obtain　higher　score　in　response　map　of　region　proposal　network,　and　thus　realize　target　instance-specific　tracking　task.　Experiments　on　two　public　benchmark　datasets,　OTB2015　and　VOT2018,　show　that　the　proposed　tracker　has　a　competitive　performance　among　other　state-of-the　art　trackers.