In object tracking tasks， Fully-Convolutional Siamese network for object tracking （SiamFC） algorithm has problems such as poor robustness and loss of tracking objects under the scenes of object occlusion and illumination variation. Therefore， an object tracking algorithm combining attention mechanism and feature fusion was proposed. Firstly， ResNet50 （Deep Residual Network） was used as the backbone network to extract more adequate object features. Secondly， attention mechanism was used to filter features. After low-level template features and high-level template features were correlated with the corresponding search features， the adaptive weighted fusion was carried out to improve the discrimination of positive and negative samples. Tested on the OTB100 （Object Tracking Benchmark） dataset， the proposed algorithm had the precision and success rate of 81.25% and 64.06%. Tested on the LaSOT （high-quality benchmark for Large-scale Single Object Tracking） dataset， the proposed algorithm had the precision and success rate of 49.4% and 50.1%. Experimental results show that the object tracking performance of the proposed algorithm is better than that of the fully convolutional Siamese network algorithm， and it has better robustness when dealing with complex scenes.