The tracking algorithms based on the Siamese networks show great potential in terms of tracking accuracy and speed. However, it is still challenging to adapt the offline trained model to online tracking. In order to improve the feature extraction and discrimination ability of the algorithm in complex scenes, a Siamese network real-time tracking algorithm that combines channel, interconnection and spatial attention mechanisms was proposed. First a Siamese tracking framework with a deep convolutional network VGG-Net-16 as the backbone network was built to increase feature extraction capabilities; then the channel-interconnection-spatial attention module was integrated to enhance the adaptability and discrimination capabilities of the model; then the multi-layer response maps were weighted and fused to obtain more accurate tracking results; and finally the large-scale datasets were used to train the end-to-end network, and tracking test on the benchmark OTB-2015 was completed. The experimental results show that compared with the current mainstream algorithms, the proposed algorithm is more robust and better adapt to complex scenes such as target appearance changes, similar distractors, and occlusion. On the NVIDIA RTX 2060 GPU, the average tracking speed reaches 37FPS, which meets real-time requirements.